diff --git a/clausiepy/README.md b/clausiepy/README.md new file mode 100644 index 00000000..046a04f2 --- /dev/null +++ b/clausiepy/README.md @@ -0,0 +1,103 @@ +# clausiepy +Implementation of the ClausIE information extraction system for python+spacy + +## Credits +While this is a re-implementation by me, original research work (and also the dictionaries) is attributed to Luciano Del Corro +and Rainer Gemulla. If you use it in your code please note that there are slight modifications in the code in order to make it work with the spacy dependency parser, and also cite: +``` +Del Corro Luciano, and Rainer Gemulla: "Clausie: clause-based open information extraction." +Proceedings of the 22nd international conference on World Wide Web. ACM, 2013. +``` + +It would be helpful to also cite this specific implementation if you are using it: +``` +@InProceedings{chourdakis2018grammar, +author = {Chourdakis, E.T and Reiss, J.D.}, +title = {Grammar Informed Sound Effect Retrieval for Soundscape Generation}, +booktitle = {DMRN+ 13: Digital Music Research Network One-day Workshop}, +month = {November}, +year = {2018}, +address = {London, UK}, +pages={9} +} +``` + +## Requirements +`spacy>=2.0.0` + +## Installation +``` +$ git clone https://github.com/mmxgn/clausiepy.git +$ cd clausiepy +$ python3 setup.py build +$ python3 setup.py install [--user] +``` + +## Usage + +### Python + +``` +$ ipython3 + +In [1]: import clausiepy as clausie +In [2]: clauses = clausie.clausie('Albert Einstein died in Princeton in 1955.') +In [3]: clauses +Out[3]: +[{'S': [Einstein], + 'V': [died], + 'O': [], + 'IO': [], + 'XCOMP': [], + 'C': [], + 'type': 'SV', + 'A?': [in, in]}] +In [4]: propositions = clausie.extract_propositions(clauses) +In [5]: clausie.print_propositions(propositions) +Out [5]: +([Einstein], [died], [], [], [], []) +([Einstein], [died], [], [], [], [in, Princeton]) +([Einstein], [died], [], [], [], [in, 1955]) +``` +Note that `clausie`, and `extract_propositions` here return dictionaries and lists of `spacy` span objects which you +can subsequently use however you like. + +### Problog + +Copy `problog/clausiepy_pl.py` at the same directory as your problog `.pl` files, include it +in your scripts with: + +``` +:- use_module('clausiepy_pl.py'). +``` + +And use it via the `clausie/7` predicate. An example can be seen in `problog/test_clausie.pl`: + +``` +:-use_module('clausiepy_pl.py'). + +query(clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.', Subject, Verb, IndirectObject, DirectObject, Complement, Adverb)). + +``` + +You can run it with: + +``` +problog test_clausie.pl +``` + +and get the output: + +``` + clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.',Einstein,died,,,,): 1 + clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.',Einstein,died,,,,in 1955): 1 + clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.',Einstein,died,,,,in Princeton): 1 +clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.',Einstein,is,,,a scientist of the 20th century,): 1 +``` + +The variables `Subject`, `Verb`, etc. are self explanatory. + + +## License + +This code is licensed under the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/). diff --git a/clausiepy/build/lib/clausiepy/__init__.py b/clausiepy/build/lib/clausiepy/__init__.py new file mode 100644 index 00000000..535ea409 --- /dev/null +++ b/clausiepy/build/lib/clausiepy/__init__.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 25 17:57:27 2018 + +@author: Emmanouil Theofanis Chourdakis + +Reimplementation in spacy+python of: + +Del Corro Luciano, and Rainer Gemulla. +"Clausie: clause-based open information extraction." +Proceedings of the 22nd international conference on World Wide Web. ACM, 2013. + +""" + +from .clausiepy import * \ No newline at end of file diff --git a/clausiepy/build/lib/clausiepy/clausiepy.py b/clausiepy/build/lib/clausiepy/clausiepy.py new file mode 100644 index 00000000..1a8f7238 --- /dev/null +++ b/clausiepy/build/lib/clausiepy/clausiepy.py @@ -0,0 +1,640 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 25 17:57:27 2018 + +@author: Emmanouil Theofanis Chourdakis + +Reimplementation in spacy+python of: + +Del Corro Luciano, and Rainer Gemulla. +"Clausie: clause-based open information extraction." +Proceedings of the 22nd international conference on World Wide Web. ACM, 2013. + +""" + +import os +dirpath = os.path.dirname(os.path.realpath(__file__)) + +# Load NLP model +import spacy +from spacy import displacy +nlp = spacy.load('en') + +# Dictionaries +dict_non_ext_copular = ['die', 'walk'] +dict_ext_copular = ['act', + 'appear', + 'be', + 'become', + 'come', + 'come out', + 'end up', + 'get', + 'go', + 'grow', + 'fall', + 'feel', + 'keep', + 'leave', + 'look', + 'prove', + 'remain', + 'seem', + 'smell', + 'sound', + 'stay', + 'taste', + 'turn', + 'turn up', + 'wind up', + 'live', + 'come', + 'go', + 'stand', + 'lie', + 'love', + 'do', + 'try'] + +dict_copular = ['act', + 'appear', + 'be', + 'become', + 'come', + 'come out', + 'end up', + 'get', + 'go', + 'grow', + 'fall', + 'feel', + 'keep', + 'leave', + 'look', + 'prove', + 'remain', + 'seem', + 'smell', + 'sound', + 'stay', + 'taste', + 'turn', + 'turn up', + 'wind up'] + +dict_complex_transitive = ['bring', + 'catch', + 'drive', + 'get', + 'keep', + 'lay', + 'lead', + 'place', + 'put', + 'set', + 'sit', + 'show', + 'stand', + 'slip', + 'take'] + + +dict_ignore = ['so', 'then', 'thus', 'why', 'as', 'even'] + + +def translate_clause(clause): + """ Modifies clause so that relative clause indicators (whose, which, where) + are resolved before subsequent processing + """ + + for n, token in enumerate(clause['S']): + # If you have a "which" or a "whose", replace it with the token pointed + # by the relcl dependency of the antidescendant. + + if token.text.lower() in ['which', 'who']: + if token.head.dep_ == 'relcl': + clause['S'].remove(token) + clause['S'].insert(0, token.head.head) + + if 'A' in clause: + for n, token in enumerate(clause['A']): + if token.text.lower() in ['where']: + if token.head.dep_ == 'relcl': + clause['A'].remove(token) + clause['A'].insert(0, token.head.head.head) + + if 'A?' in clause: + for n, token in enumerate(clause['A?']): + if token.text.lower() in ['where']: + if token.head.dep_ == 'relcl': + clause['A?'].remove(token) + clause['A?'].insert(0, token.head.head.head) + + return clause + + +def empty_clause(): + return {'S':[], 'V':[], 'O':[], 'IO': [], 'XCOMP': [], 'A':[], 'C':[]} + +def has_object(clause): + return has_dobj(clause) or has_iobj(clause) + +def has_dobj(clause): + return len(clause['O']) > 0 + +def has_iobj(clause): + return len(clause['IO']) > 0 + +def has_complement(clause): + return len(clause['C']) > 0 or len(clause['XCOMP']) > 0 + +def has_candidate_adverbial(clause): + for verb in clause['V']: + for adv in clause['A']: + if adv in verb.subtree and adv.i > verb.i: + return True + + else: + return False + +def has_known_non_ext_copular(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_non_ext_copular: + return True + else: + return False + +def has_known_ext_copular(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_ext_copular: + return True + else: + return False + +def is_known_ext_copular(verb): + return nlp(verb.text)[0].lemma_ in dict_ext_copular + + +def is_known_copular(verb): + return str(verb) in dict_copular + +def has_potentially_complex_transitive(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_complex_transitive: + return True + else: + return False + +def is_in_ignore_list(adverb): + return nlp(adverb.text)[0].lemma_ in dict_ignore + +def clausie(sent, conservative=True): + + def process_dependants(token, clause): + dependants = [c for c in token.head.subtree if c not in token.subtree] + for d in dependants: + if d.dep_ in ['dobj']: + clause['O'].append(d) + elif d.dep_ in ['iobj', 'dative']: + clause['IO'].append(d) + elif d.dep_ in ['ccomp', 'acomp', 'attr']: + clause['C'].append(d) + + elif d.dep_ in ['xcomp']: + if is_known_copular(d): + clause['XCOMP'].append(d.head) + else: + clause['O'].append(d) + elif d.dep_ in ['advmod', 'advcl', 'npadvmod']: + clause['A'].append(d) + elif d.dep_ in ['oprd'] and d.head in clause['V']: + clause['A'].append(d) + elif d.dep_ in ['prep']: + # Capture "prep_in(X, Y)". + # which is prep(X, in) and pobj(in, Y) + for c in d.children: + if c.dep_ == 'pobj': + # clause['A'].append(c) + clause['A'].append(d) + + doc = nlp(sent) + ents = doc.ents + print(ents) + clauses = [] + + clause = empty_clause() + + # Check if root is not a verb + root = [t for t in doc if t.dep_ == 'ROOT'][0] + if root.pos_ != 'VERB': + doc = nlp("There is " + sent) + + # Subjects of a verb + for token in doc: + # print("{}({},{})".format(token.dep_, token.head, token)) + if token.dep_ in ['nsubj', 'nsubjpass', 'attr']: + clause['S'].append(token) + clause['V'].append(token.head) + + # Take dependants: + process_dependants(token, clause) + + clauses.append(translate_clause(clause)) + clause = empty_clause() + + elif token.dep_ in ['csubj']: + clause['S'].append(token) + clause['V'].append(token.head) + + # Take dependants: + dependants = [c for c in token.head.subtree if c not in token.subtree] + #dependants = token.head.children + for d in dependants: + if d.dep_ in ['dobj']: + clause['O'].append(d) + + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['appos']: + # Subjects without a verb + # E.g. Sam is my brother in: Sam, my brother. + clause['S'].append(token.head) + clause['V'].append(nlp('is')[0]) + clause['C'].append(token) + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['poss']: + # Subjects declaring possesion + # E.g. my brother: in: Sam, my brother. + toktext = token.text + if token.text.lower() == 'his': + clause['S'].append(nlp('he')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'her': + clause['S'].append(nlp('she')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'my': + clause['S'].append(nlp('I')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'its': + clause['S'].append(nlp('it')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'our': + clause['S'].append(nlp('we')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'your': + clause['S'].append(nlp('you')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'their': + clause['S'].append(nlp('they')[0]) + clause['V'].append(nlp('have')[0]) + else: + clause['S'].append(token) + clause['V'].append(nlp('has')[0]) + clause['O'].append(token.head) + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['acl']: + # Create a synthetic from participial modifiers (partmod). + clause['S'].append(token.head) + new_sent = nlp("are {}".format(" ".join([t.text for t in token.subtree]))) + r = [t for t in new_sent if t.dep_ == 'ROOT'][0] + clause['V'].append(r) + + process_dependants(token, clause) + + + clauses.append(translate_clause(clause)) + clause = empty_clause() + + # Identify clause types + for clause in clauses: + type_ = 'OTHER' + if not has_object(clause): # Q1 + if has_complement(clause): #Q2 + type_ = 'SVC' + else: + # Q3 + if not has_candidate_adverbial(clause): + type_ = 'SV' + else: + # Q4 + if has_known_non_ext_copular(clause): + type_ = 'SV' + else: + # Q5 + if has_known_ext_copular(clause): + type_ = 'SVA' + else: + # Q6: Cases we want conservative or non-conservative estimation + if conservative: + type_ = 'SVA' + else: + type_ = 'SV' + + else: + # Q7 + if has_dobj(clause) and has_iobj(clause): + type_ = 'SVOO' + else: + # Q8 + if has_complement(clause): + type_ = 'SVOC' + else: + #Q9 + if not has_candidate_adverbial(clause) and has_dobj(clause): + type_ = 'SVO' + else: + # Q10 + if has_potentially_complex_transitive(clause): + type_ = 'SVOA' + else: + # Q11 + if conservative: + type_ = 'SVOA' + else: + type_ = 'SVO' + + clause['type'] = type_ + + if type_ in ['SVC', 'SVOO', 'SVOC', 'SV', 'SVO']: + clause['A?'] = clause['A'] + clause.pop('A', None) + + return clauses, ents + +def append_conjugates(L): + for l in L: + if type(l) == str: + continue + for c in l.children: + if c.dep_ in ['conj']: + L.append(c) + +def extract_propositions(clauses): + propositions = [] + for clause in clauses: + + subjects = clause['S'] + append_conjugates(subjects) + + verbs = clause['V'] + append_conjugates(verbs) + + type_ = clause['type'] + + for s in subjects: + for v in verbs: + + if type(v) == str: + v = nlp(v)[0] + + prop = (s, v) + + if type_ in ['SV']: + if prop not in propositions: + if v.text in ['is' ,'are']: + propositions.append({'subject':s, 'verb':nlp("exists")[0]}) + else: + propositions.append({'subject': s, 'verb':v}) + + + adverbs = clause['A?'] + append_conjugates(adverbs) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVO']: + objects = clause['O'] + append_conjugates(objects) + adverbs = clause['A?'] + append_conjugates(adverbs) + + for o in objects: + + + prop = {'subject': s, 'verb':v, 'direct object':o} + if prop not in propositions: + propositions.append(prop) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'direct object':o, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + + # Extractions of form: + # AE had a faboulous hairstyle -> Hairstyle was faboulous + for c in o.children: + if c.dep_ == 'amod': + prop = {'subject': o, 'verb':[t for t in nlp('is')][0], 'complement':c} + if prop not in propositions: + propositions.append(prop) + + + elif type_ in ['SVA']: + adverbs = clause['A'] + append_conjugates(adverbs) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVC']: + comp = clause['C'] + adverbs = clause['A?'] + append_conjugates(adverbs) + append_conjugates(comp) + for c in comp: + prop = {'subject': s, 'verb':v, 'complement':c} + if prop not in propositions: + propositions.append(prop) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'complement':c, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVOO']: + dobjects = clause['O'] + iobjects = clause['IO'] + + append_conjugates(dobjects) + append_conjugates(iobjects) + adverbs = clause['A?'] + append_conjugates(adverbs) + + for io in iobjects: + for do in dobjects: + prop = {'subject': s, 'verb':v, 'indirect object':io, 'direct object':do} + if prop not in propositions: + propositions.append(prop) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'indirect object':io, 'direct object':do, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + + elif type_ in ['SVOA']: + dobjects = clause['O'] + append_conjugates(dobjects) + adverbs = clause['A'] + append_conjugates(adverbs) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVOC']: + dobjects = clause['O'] + append_conjugates(dobjects) + + comp = clause['C'] + append_conjugates(comp) + + adverbs = clause['A?'] + append_conjugates(adverbs) + + for c in comp: + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'complement': c} + + if prop not in propositions: + propositions.append(prop) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'complement':c, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + return propositions + +def get_conj_text(token): + L = [token] + token_old = None + while token_old != token: + token_old = token + for c in token.children: + if c.dep_ in ['cc', 'punct']: + L.append(c) + if c.dep_ == 'conj': + L.append(c) + token = c + break + + return " ".join([t.text for t in L]) + +def proposition_text(prop): + + # subject = [t for t in prop['subject'].children if t.dep_ in ['det', 'amod']] + [prop['subject']] + subject = [t for t in prop['subject'].lefts] + [prop['subject']] + + # Add of the + for t in prop['subject'].rights: + if t.dep_ in ['prep']: + subject += [d for d in t.subtree] + + + + if 'indirect object' in prop: + indirect_object = [t for t in prop['indirect object'].children if t.dep_ in ['det', 'amod', 'compound']] + [prop['indirect object']] + else: + indirect_object = [] + + if 'direct object' in prop: + direct_object = [t for t in prop['direct object'].children if t.dep_ in ['det', 'amod', 'compound']] + [prop['direct object']] + else: + direct_object = [] + + if 'complement' in prop: + complement = [t for t in prop['complement'].subtree] + else: + complement = [] + + if 'adverb' in prop: + adv = prop['adverb'] + if adv.dep_ == 'pobj' and adv.head.dep_ =='prep': + # Prepositional phrase + adverb = [adv.head] + [t for t in prop['adverb'].subtree] + elif adv.dep_ == 'advmod' and adv.head.dep_ == 'npadvmod': + adverb = [t for t in adv.head.subtree] + else: + adverb = [t for t in prop['adverb'].subtree] + else: + adverb = [] + + verb_aux = [p for p in prop['verb'].lefts if p.dep_ in ['aux', 'auxpass']] + verb = verb_aux+[prop['verb']] + + return subject , verb , indirect_object , direct_object , complement , adverb + +def proposition_text_str(prop): + """ Like proposition_text(prop) but returns a string isntead """ + L = proposition_text(prop) + + str_list = [] + + for l in L: + if len(l)>0: + str_list += l + + return " ".join([t.text for t in str_list]) + " ." + +def print_propositions(plist): + for prop in plist: + text = proposition_text(prop) + print(text) + +if __name__ == "__main__": + + from util import * + + print("Testing with various sentences") + sentences = [ + "Bell , a telecommunication company based in Los Angeles , makes and distributes electronic , computer and building products", + "AE died.", + "AE remained in Princeton.", + "AE is smart.", + "AE has won the Nobel Prize.", + "RSAS gave AE the Nobel Prize.", + "The doorman showed AE to his office .", + "AE declared the meeting open .", + "AE died in Princeton in 1955 .", + "AE remained in Princeton until his death .", + "AE is a scientist of the 20th century .", + "AE has won the Nobel Prize in 1921 .", + "In 1921, AE has won the Nobel Prize . ", + "Nicolas Cage graciously ate and enjoyed the blue fruit and the yellow steak.", + "A bull was feeding in a meadow until a lion approached the bull", + "The attack of the lion caused the death of the bull.", + "Some crows are eating rubbish at a garbage dump.", + "AE knocked the door three times.", + "All crows have a beak.", + "AE had a faboulous hairstyle.", + ] + + for sent in sentences: + print("Sentence:") + print(sent) + print("Dependencies:") + tree_from_doc(nlp(sent)).show() + print("Clauses:") + clauses = clausie(sent) + print() + for clause in clauses: + print("\t{}".format(clause)) + print() + print("Propositions:") + propositions = extract_propositions(clauses) + for prop in propositions: + print(proposition_text_str(prop)) + #print_propositions(propositions) + + print("-----") + diff --git a/clausiepy/build/lib/clausiepy/util.py b/clausiepy/build/lib/clausiepy/util.py new file mode 100644 index 00000000..ee1a52fe --- /dev/null +++ b/clausiepy/build/lib/clausiepy/util.py @@ -0,0 +1,26 @@ +from pprint import pprint +from treelib import Node, Tree + +def tree_from_token(root): + def add_children(root, tree): + for c in root.children: + tree.create_node("{}:{}/{}".format(c.dep_,c,c.pos_), hash(c), parent=hash(root), data=c) + add_children(c, tree) + + tree = Tree() + tree.create_node("{}/{}".format(root, root.pos_), hash(root), data=root) + add_children(root, tree) + return tree + + + +def tree_from_doc(doc): + # 1. Find root + root = [t for t in doc if t.dep_ == 'ROOT'][0] + return tree_from_token(root) + +def tree_from_annotation(annot): + doc = nlp(annot[0]) + labels = annot[1]['entities'] + root = [t for t in doc if t.dep_ == 'ROOT'][0] + return tree_from_token_with_labels(root, labels) diff --git a/clausiepy/clausiepy.egg-info/PKG-INFO b/clausiepy/clausiepy.egg-info/PKG-INFO new file mode 100644 index 00000000..099ba898 --- /dev/null +++ b/clausiepy/clausiepy.egg-info/PKG-INFO @@ -0,0 +1,11 @@ +Metadata-Version: 1.0 +Name: clausiepy +Version: 0.0.1 +Summary: A reimplementation of ClausIE Information Extraction System in python +Home-page: https://github.com/mmxgn/clausiepy +Author: Emmanouil Theofanis Chourdakis +Author-email: e.t.chourdakis@qmul.ac.uk +License: UNKNOWN +Description: UNKNOWN +Keywords: openie clausie information extraction +Platform: UNKNOWN diff --git a/clausiepy/clausiepy.egg-info/SOURCES.txt b/clausiepy/clausiepy.egg-info/SOURCES.txt new file mode 100644 index 00000000..aef220ae --- /dev/null +++ b/clausiepy/clausiepy.egg-info/SOURCES.txt @@ -0,0 +1,10 @@ +README.md +setup.py +clausiepy/__init__.py +clausiepy/clausiepy.py +clausiepy/util.py +clausiepy.egg-info/PKG-INFO +clausiepy.egg-info/SOURCES.txt +clausiepy.egg-info/dependency_links.txt +clausiepy.egg-info/requires.txt +clausiepy.egg-info/top_level.txt \ No newline at end of file diff --git a/clausiepy/clausiepy.egg-info/dependency_links.txt b/clausiepy/clausiepy.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/clausiepy/clausiepy.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/clausiepy/clausiepy.egg-info/requires.txt b/clausiepy/clausiepy.egg-info/requires.txt new file mode 100644 index 00000000..f8fd1638 --- /dev/null +++ b/clausiepy/clausiepy.egg-info/requires.txt @@ -0,0 +1 @@ +spacy>=2.0.0 diff --git a/clausiepy/clausiepy.egg-info/top_level.txt b/clausiepy/clausiepy.egg-info/top_level.txt new file mode 100644 index 00000000..bbee788a --- /dev/null +++ b/clausiepy/clausiepy.egg-info/top_level.txt @@ -0,0 +1 @@ +clausiepy diff --git a/clausiepy/clausiepy/__init__.py b/clausiepy/clausiepy/__init__.py new file mode 100644 index 00000000..535ea409 --- /dev/null +++ b/clausiepy/clausiepy/__init__.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 25 17:57:27 2018 + +@author: Emmanouil Theofanis Chourdakis + +Reimplementation in spacy+python of: + +Del Corro Luciano, and Rainer Gemulla. +"Clausie: clause-based open information extraction." +Proceedings of the 22nd international conference on World Wide Web. ACM, 2013. + +""" + +from .clausiepy import * \ No newline at end of file diff --git a/clausiepy/clausiepy/clausiepy.py b/clausiepy/clausiepy/clausiepy.py new file mode 100644 index 00000000..1a8f7238 --- /dev/null +++ b/clausiepy/clausiepy/clausiepy.py @@ -0,0 +1,640 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 25 17:57:27 2018 + +@author: Emmanouil Theofanis Chourdakis + +Reimplementation in spacy+python of: + +Del Corro Luciano, and Rainer Gemulla. +"Clausie: clause-based open information extraction." +Proceedings of the 22nd international conference on World Wide Web. ACM, 2013. + +""" + +import os +dirpath = os.path.dirname(os.path.realpath(__file__)) + +# Load NLP model +import spacy +from spacy import displacy +nlp = spacy.load('en') + +# Dictionaries +dict_non_ext_copular = ['die', 'walk'] +dict_ext_copular = ['act', + 'appear', + 'be', + 'become', + 'come', + 'come out', + 'end up', + 'get', + 'go', + 'grow', + 'fall', + 'feel', + 'keep', + 'leave', + 'look', + 'prove', + 'remain', + 'seem', + 'smell', + 'sound', + 'stay', + 'taste', + 'turn', + 'turn up', + 'wind up', + 'live', + 'come', + 'go', + 'stand', + 'lie', + 'love', + 'do', + 'try'] + +dict_copular = ['act', + 'appear', + 'be', + 'become', + 'come', + 'come out', + 'end up', + 'get', + 'go', + 'grow', + 'fall', + 'feel', + 'keep', + 'leave', + 'look', + 'prove', + 'remain', + 'seem', + 'smell', + 'sound', + 'stay', + 'taste', + 'turn', + 'turn up', + 'wind up'] + +dict_complex_transitive = ['bring', + 'catch', + 'drive', + 'get', + 'keep', + 'lay', + 'lead', + 'place', + 'put', + 'set', + 'sit', + 'show', + 'stand', + 'slip', + 'take'] + + +dict_ignore = ['so', 'then', 'thus', 'why', 'as', 'even'] + + +def translate_clause(clause): + """ Modifies clause so that relative clause indicators (whose, which, where) + are resolved before subsequent processing + """ + + for n, token in enumerate(clause['S']): + # If you have a "which" or a "whose", replace it with the token pointed + # by the relcl dependency of the antidescendant. + + if token.text.lower() in ['which', 'who']: + if token.head.dep_ == 'relcl': + clause['S'].remove(token) + clause['S'].insert(0, token.head.head) + + if 'A' in clause: + for n, token in enumerate(clause['A']): + if token.text.lower() in ['where']: + if token.head.dep_ == 'relcl': + clause['A'].remove(token) + clause['A'].insert(0, token.head.head.head) + + if 'A?' in clause: + for n, token in enumerate(clause['A?']): + if token.text.lower() in ['where']: + if token.head.dep_ == 'relcl': + clause['A?'].remove(token) + clause['A?'].insert(0, token.head.head.head) + + return clause + + +def empty_clause(): + return {'S':[], 'V':[], 'O':[], 'IO': [], 'XCOMP': [], 'A':[], 'C':[]} + +def has_object(clause): + return has_dobj(clause) or has_iobj(clause) + +def has_dobj(clause): + return len(clause['O']) > 0 + +def has_iobj(clause): + return len(clause['IO']) > 0 + +def has_complement(clause): + return len(clause['C']) > 0 or len(clause['XCOMP']) > 0 + +def has_candidate_adverbial(clause): + for verb in clause['V']: + for adv in clause['A']: + if adv in verb.subtree and adv.i > verb.i: + return True + + else: + return False + +def has_known_non_ext_copular(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_non_ext_copular: + return True + else: + return False + +def has_known_ext_copular(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_ext_copular: + return True + else: + return False + +def is_known_ext_copular(verb): + return nlp(verb.text)[0].lemma_ in dict_ext_copular + + +def is_known_copular(verb): + return str(verb) in dict_copular + +def has_potentially_complex_transitive(clause): + for verb in clause['V']: + if nlp(verb.text)[0].lemma_ in dict_complex_transitive: + return True + else: + return False + +def is_in_ignore_list(adverb): + return nlp(adverb.text)[0].lemma_ in dict_ignore + +def clausie(sent, conservative=True): + + def process_dependants(token, clause): + dependants = [c for c in token.head.subtree if c not in token.subtree] + for d in dependants: + if d.dep_ in ['dobj']: + clause['O'].append(d) + elif d.dep_ in ['iobj', 'dative']: + clause['IO'].append(d) + elif d.dep_ in ['ccomp', 'acomp', 'attr']: + clause['C'].append(d) + + elif d.dep_ in ['xcomp']: + if is_known_copular(d): + clause['XCOMP'].append(d.head) + else: + clause['O'].append(d) + elif d.dep_ in ['advmod', 'advcl', 'npadvmod']: + clause['A'].append(d) + elif d.dep_ in ['oprd'] and d.head in clause['V']: + clause['A'].append(d) + elif d.dep_ in ['prep']: + # Capture "prep_in(X, Y)". + # which is prep(X, in) and pobj(in, Y) + for c in d.children: + if c.dep_ == 'pobj': + # clause['A'].append(c) + clause['A'].append(d) + + doc = nlp(sent) + ents = doc.ents + print(ents) + clauses = [] + + clause = empty_clause() + + # Check if root is not a verb + root = [t for t in doc if t.dep_ == 'ROOT'][0] + if root.pos_ != 'VERB': + doc = nlp("There is " + sent) + + # Subjects of a verb + for token in doc: + # print("{}({},{})".format(token.dep_, token.head, token)) + if token.dep_ in ['nsubj', 'nsubjpass', 'attr']: + clause['S'].append(token) + clause['V'].append(token.head) + + # Take dependants: + process_dependants(token, clause) + + clauses.append(translate_clause(clause)) + clause = empty_clause() + + elif token.dep_ in ['csubj']: + clause['S'].append(token) + clause['V'].append(token.head) + + # Take dependants: + dependants = [c for c in token.head.subtree if c not in token.subtree] + #dependants = token.head.children + for d in dependants: + if d.dep_ in ['dobj']: + clause['O'].append(d) + + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['appos']: + # Subjects without a verb + # E.g. Sam is my brother in: Sam, my brother. + clause['S'].append(token.head) + clause['V'].append(nlp('is')[0]) + clause['C'].append(token) + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['poss']: + # Subjects declaring possesion + # E.g. my brother: in: Sam, my brother. + toktext = token.text + if token.text.lower() == 'his': + clause['S'].append(nlp('he')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'her': + clause['S'].append(nlp('she')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'my': + clause['S'].append(nlp('I')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'its': + clause['S'].append(nlp('it')[0]) + clause['V'].append(nlp('has')[0]) + elif token.text.lower() == 'our': + clause['S'].append(nlp('we')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'your': + clause['S'].append(nlp('you')[0]) + clause['V'].append(nlp('have')[0]) + elif token.text.lower() == 'their': + clause['S'].append(nlp('they')[0]) + clause['V'].append(nlp('have')[0]) + else: + clause['S'].append(token) + clause['V'].append(nlp('has')[0]) + clause['O'].append(token.head) + clauses.append(translate_clause(clause)) + clause = empty_clause() + elif token.dep_ in ['acl']: + # Create a synthetic from participial modifiers (partmod). + clause['S'].append(token.head) + new_sent = nlp("are {}".format(" ".join([t.text for t in token.subtree]))) + r = [t for t in new_sent if t.dep_ == 'ROOT'][0] + clause['V'].append(r) + + process_dependants(token, clause) + + + clauses.append(translate_clause(clause)) + clause = empty_clause() + + # Identify clause types + for clause in clauses: + type_ = 'OTHER' + if not has_object(clause): # Q1 + if has_complement(clause): #Q2 + type_ = 'SVC' + else: + # Q3 + if not has_candidate_adverbial(clause): + type_ = 'SV' + else: + # Q4 + if has_known_non_ext_copular(clause): + type_ = 'SV' + else: + # Q5 + if has_known_ext_copular(clause): + type_ = 'SVA' + else: + # Q6: Cases we want conservative or non-conservative estimation + if conservative: + type_ = 'SVA' + else: + type_ = 'SV' + + else: + # Q7 + if has_dobj(clause) and has_iobj(clause): + type_ = 'SVOO' + else: + # Q8 + if has_complement(clause): + type_ = 'SVOC' + else: + #Q9 + if not has_candidate_adverbial(clause) and has_dobj(clause): + type_ = 'SVO' + else: + # Q10 + if has_potentially_complex_transitive(clause): + type_ = 'SVOA' + else: + # Q11 + if conservative: + type_ = 'SVOA' + else: + type_ = 'SVO' + + clause['type'] = type_ + + if type_ in ['SVC', 'SVOO', 'SVOC', 'SV', 'SVO']: + clause['A?'] = clause['A'] + clause.pop('A', None) + + return clauses, ents + +def append_conjugates(L): + for l in L: + if type(l) == str: + continue + for c in l.children: + if c.dep_ in ['conj']: + L.append(c) + +def extract_propositions(clauses): + propositions = [] + for clause in clauses: + + subjects = clause['S'] + append_conjugates(subjects) + + verbs = clause['V'] + append_conjugates(verbs) + + type_ = clause['type'] + + for s in subjects: + for v in verbs: + + if type(v) == str: + v = nlp(v)[0] + + prop = (s, v) + + if type_ in ['SV']: + if prop not in propositions: + if v.text in ['is' ,'are']: + propositions.append({'subject':s, 'verb':nlp("exists")[0]}) + else: + propositions.append({'subject': s, 'verb':v}) + + + adverbs = clause['A?'] + append_conjugates(adverbs) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVO']: + objects = clause['O'] + append_conjugates(objects) + adverbs = clause['A?'] + append_conjugates(adverbs) + + for o in objects: + + + prop = {'subject': s, 'verb':v, 'direct object':o} + if prop not in propositions: + propositions.append(prop) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'direct object':o, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + + # Extractions of form: + # AE had a faboulous hairstyle -> Hairstyle was faboulous + for c in o.children: + if c.dep_ == 'amod': + prop = {'subject': o, 'verb':[t for t in nlp('is')][0], 'complement':c} + if prop not in propositions: + propositions.append(prop) + + + elif type_ in ['SVA']: + adverbs = clause['A'] + append_conjugates(adverbs) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVC']: + comp = clause['C'] + adverbs = clause['A?'] + append_conjugates(adverbs) + append_conjugates(comp) + for c in comp: + prop = {'subject': s, 'verb':v, 'complement':c} + if prop not in propositions: + propositions.append(prop) + for a in adverbs: + if not is_in_ignore_list(a): + prop = {'subject': s, 'verb':v, 'complement':c, 'adverb':a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVOO']: + dobjects = clause['O'] + iobjects = clause['IO'] + + append_conjugates(dobjects) + append_conjugates(iobjects) + adverbs = clause['A?'] + append_conjugates(adverbs) + + for io in iobjects: + for do in dobjects: + prop = {'subject': s, 'verb':v, 'indirect object':io, 'direct object':do} + if prop not in propositions: + propositions.append(prop) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'indirect object':io, 'direct object':do, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + + elif type_ in ['SVOA']: + dobjects = clause['O'] + append_conjugates(dobjects) + adverbs = clause['A'] + append_conjugates(adverbs) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + elif type_ in ['SVOC']: + dobjects = clause['O'] + append_conjugates(dobjects) + + comp = clause['C'] + append_conjugates(comp) + + adverbs = clause['A?'] + append_conjugates(adverbs) + + for c in comp: + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'complement': c} + + if prop not in propositions: + propositions.append(prop) + + for a in adverbs: + if not is_in_ignore_list(a): + for do in dobjects: + prop = {'subject': s, 'verb':v, 'direct object':do, 'complement':c, 'adverb': a} + if prop not in propositions: + propositions.append(prop) + return propositions + +def get_conj_text(token): + L = [token] + token_old = None + while token_old != token: + token_old = token + for c in token.children: + if c.dep_ in ['cc', 'punct']: + L.append(c) + if c.dep_ == 'conj': + L.append(c) + token = c + break + + return " ".join([t.text for t in L]) + +def proposition_text(prop): + + # subject = [t for t in prop['subject'].children if t.dep_ in ['det', 'amod']] + [prop['subject']] + subject = [t for t in prop['subject'].lefts] + [prop['subject']] + + # Add of the + for t in prop['subject'].rights: + if t.dep_ in ['prep']: + subject += [d for d in t.subtree] + + + + if 'indirect object' in prop: + indirect_object = [t for t in prop['indirect object'].children if t.dep_ in ['det', 'amod', 'compound']] + [prop['indirect object']] + else: + indirect_object = [] + + if 'direct object' in prop: + direct_object = [t for t in prop['direct object'].children if t.dep_ in ['det', 'amod', 'compound']] + [prop['direct object']] + else: + direct_object = [] + + if 'complement' in prop: + complement = [t for t in prop['complement'].subtree] + else: + complement = [] + + if 'adverb' in prop: + adv = prop['adverb'] + if adv.dep_ == 'pobj' and adv.head.dep_ =='prep': + # Prepositional phrase + adverb = [adv.head] + [t for t in prop['adverb'].subtree] + elif adv.dep_ == 'advmod' and adv.head.dep_ == 'npadvmod': + adverb = [t for t in adv.head.subtree] + else: + adverb = [t for t in prop['adverb'].subtree] + else: + adverb = [] + + verb_aux = [p for p in prop['verb'].lefts if p.dep_ in ['aux', 'auxpass']] + verb = verb_aux+[prop['verb']] + + return subject , verb , indirect_object , direct_object , complement , adverb + +def proposition_text_str(prop): + """ Like proposition_text(prop) but returns a string isntead """ + L = proposition_text(prop) + + str_list = [] + + for l in L: + if len(l)>0: + str_list += l + + return " ".join([t.text for t in str_list]) + " ." + +def print_propositions(plist): + for prop in plist: + text = proposition_text(prop) + print(text) + +if __name__ == "__main__": + + from util import * + + print("Testing with various sentences") + sentences = [ + "Bell , a telecommunication company based in Los Angeles , makes and distributes electronic , computer and building products", + "AE died.", + "AE remained in Princeton.", + "AE is smart.", + "AE has won the Nobel Prize.", + "RSAS gave AE the Nobel Prize.", + "The doorman showed AE to his office .", + "AE declared the meeting open .", + "AE died in Princeton in 1955 .", + "AE remained in Princeton until his death .", + "AE is a scientist of the 20th century .", + "AE has won the Nobel Prize in 1921 .", + "In 1921, AE has won the Nobel Prize . ", + "Nicolas Cage graciously ate and enjoyed the blue fruit and the yellow steak.", + "A bull was feeding in a meadow until a lion approached the bull", + "The attack of the lion caused the death of the bull.", + "Some crows are eating rubbish at a garbage dump.", + "AE knocked the door three times.", + "All crows have a beak.", + "AE had a faboulous hairstyle.", + ] + + for sent in sentences: + print("Sentence:") + print(sent) + print("Dependencies:") + tree_from_doc(nlp(sent)).show() + print("Clauses:") + clauses = clausie(sent) + print() + for clause in clauses: + print("\t{}".format(clause)) + print() + print("Propositions:") + propositions = extract_propositions(clauses) + for prop in propositions: + print(proposition_text_str(prop)) + #print_propositions(propositions) + + print("-----") + diff --git a/clausiepy/clausiepy/dictionaries/README.md b/clausiepy/clausiepy/dictionaries/README.md new file mode 100644 index 00000000..a1856c76 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/README.md @@ -0,0 +1,2 @@ +Those were taken from minie: +https://github.com/rgemulla/minie diff --git a/clausiepy/clausiepy/dictionaries/dict-adverbs-conj.txt b/clausiepy/clausiepy/dictionaries/dict-adverbs-conj.txt new file mode 100644 index 00000000..e69de29b diff --git a/clausiepy/clausiepy/dictionaries/dict-adverbs-ignore.txt b/clausiepy/clausiepy/dictionaries/dict-adverbs-ignore.txt new file mode 100644 index 00000000..6a452359 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-adverbs-ignore.txt @@ -0,0 +1,6 @@ +so +then +thus +why +as +even \ No newline at end of file diff --git a/clausiepy/clausiepy/dictionaries/dict-adverbs-include.txt b/clausiepy/clausiepy/dictionaries/dict-adverbs-include.txt new file mode 100644 index 00000000..04238a81 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-adverbs-include.txt @@ -0,0 +1,5 @@ +hardly +barely +scarcely +seldom +rarely \ No newline at end of file diff --git a/clausiepy/clausiepy/dictionaries/dict-complex-transitive.txt b/clausiepy/clausiepy/dictionaries/dict-complex-transitive.txt new file mode 100644 index 00000000..4ee35055 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-complex-transitive.txt @@ -0,0 +1,15 @@ +bring +catch +drive +get +keep +lay +lead +place +put +set +sit +show +stand +slip +take \ No newline at end of file diff --git a/clausiepy/clausiepy/dictionaries/dict-copular.txt b/clausiepy/clausiepy/dictionaries/dict-copular.txt new file mode 100644 index 00000000..f4973dad --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-copular.txt @@ -0,0 +1,25 @@ +act +appear +be +become +come +come out +end up +get +go +grow +fall +feel +keep +leave +look +prove +remain +seem +smell +sound +stay +taste +turn +turn up +wind up \ No newline at end of file diff --git a/clausiepy/clausiepy/dictionaries/dict-ext-copular.txt b/clausiepy/clausiepy/dictionaries/dict-ext-copular.txt new file mode 100644 index 00000000..9657fcb2 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-ext-copular.txt @@ -0,0 +1,33 @@ +act +appear +be +become +come +come out +end up +get +go +grow +fall +feel +keep +leave +look +prove +remain +seem +smell +sound +stay +taste +turn +turn up +wind up +live +come +go +stand +lie +love +do +try \ No newline at end of file diff --git a/clausiepy/clausiepy/dictionaries/dict-not-ext-copular.txt b/clausiepy/clausiepy/dictionaries/dict-not-ext-copular.txt new file mode 100644 index 00000000..df2d8652 --- /dev/null +++ b/clausiepy/clausiepy/dictionaries/dict-not-ext-copular.txt @@ -0,0 +1,2 @@ +die +walk \ No newline at end of file diff --git a/clausiepy/clausiepy/util.py b/clausiepy/clausiepy/util.py new file mode 100644 index 00000000..ee1a52fe --- /dev/null +++ b/clausiepy/clausiepy/util.py @@ -0,0 +1,26 @@ +from pprint import pprint +from treelib import Node, Tree + +def tree_from_token(root): + def add_children(root, tree): + for c in root.children: + tree.create_node("{}:{}/{}".format(c.dep_,c,c.pos_), hash(c), parent=hash(root), data=c) + add_children(c, tree) + + tree = Tree() + tree.create_node("{}/{}".format(root, root.pos_), hash(root), data=root) + add_children(root, tree) + return tree + + + +def tree_from_doc(doc): + # 1. Find root + root = [t for t in doc if t.dep_ == 'ROOT'][0] + return tree_from_token(root) + +def tree_from_annotation(annot): + doc = nlp(annot[0]) + labels = annot[1]['entities'] + root = [t for t in doc if t.dep_ == 'ROOT'][0] + return tree_from_token_with_labels(root, labels) diff --git a/clausiepy/dist/clausiepy-0.0.1-py3.6.egg b/clausiepy/dist/clausiepy-0.0.1-py3.6.egg new file mode 100644 index 00000000..8d928f77 Binary files /dev/null and b/clausiepy/dist/clausiepy-0.0.1-py3.6.egg differ diff --git a/clausiepy/problog/clausiepy_pl.py b/clausiepy/problog/clausiepy_pl.py new file mode 100644 index 00000000..8580edf5 --- /dev/null +++ b/clausiepy/problog/clausiepy_pl.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Sep 26 13:38:38 2018 + +@author: Emmanouil Theofanis Chourdakis + +Problog module for extracting information from a sentence using clausiepy + +""" + +from problog.extern import problog_export_nondet + +import clausiepy as cl + +def remove_apostrophe(string): + # Remove "'"S + if string[0] == "'": + string = string[1:] + if string[-1] == "'": + string = string[:-1] + + return string + +@problog_export_nondet('+str', '-str', '-str', '-str', '-str', '-str', '-str') +def clausie(sent): + + sent = remove_apostrophe(sent) + + clauses = cl.clausie(sent) + + propositions = cl.extract_propositions(clauses) + + result = [] + for proposition in propositions: + ptext = cl.proposition_text(proposition) + + prop = [] + + for p in ptext: + prop.append(" ".join([pp.text for pp in p])) + + result.append(tuple(prop)) + + return result \ No newline at end of file diff --git a/clausiepy/problog/test_clausie.pl b/clausiepy/problog/test_clausie.pl new file mode 100644 index 00000000..bdba16f7 --- /dev/null +++ b/clausiepy/problog/test_clausie.pl @@ -0,0 +1,5 @@ +:-use_module('clausiepy_pl.py'). + +query(clausie('Albert Einstein, a scientist of the 20th century, died in Princeton in 1955.', Subject, Verb, IndirectObject, DirectObject, Complement, Adverb)). + + diff --git a/clausiepy/setup.py b/clausiepy/setup.py new file mode 100644 index 00000000..45cc8846 --- /dev/null +++ b/clausiepy/setup.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Sep 25 18:21:51 2018 + +@author: Emmanouil Theofanis Chourdakis +""" + + + +from setuptools import setup, find_packages +setup( + name="clausiepy", + version="0.0.1", + packages=find_packages(), + #scripts=['clausiepy/clausiepy.py', 'clausiepy/__init__.py'], + install_requires=['spacy>=2.0.0'], + + author="Emmanouil Theofanis Chourdakis", + author_email="e.t.chourdakis@qmul.ac.uk", + description="A reimplementation of ClausIE Information Extraction System in python", + url="https://github.com/mmxgn/clausiepy", + keywords="openie clausie information extraction", + include_package_data=True, + +) diff --git a/data/dev_concatenation.jsonl b/data/dev_concatenation.jsonl index 91152279..428f33e4 100644 --- a/data/dev_concatenation.jsonl +++ b/data/dev_concatenation.jsonl @@ -1,116 +1,9999 @@ -{"id": 91198, "claim": "Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League.", "predicted_pages": ["49ers–Seahawks_rivalry", "Colin_Kaepernick", "2016_San_Francisco_49ers_season", "2014_San_Francisco_49ers_season", "Pistol_offense"], "predicted_sentences": [["49ers–Seahawks_rivalry", 0], ["2016_San_Francisco_49ers_season", 0], ["2014_San_Francisco_49ers_season", 0], ["Pistol_offense", 18], ["Colin_Kaepernick", 6]], "predicted_pages_ner": ["Colin_Kaepernick", "The_Cry_of_Reason", "Czech_National_Football_League"], "predicted_sentences_ner": [["Colin_Kaepernick", 0], ["Colin_Kaepernick", 1], ["Colin_Kaepernick", 2], ["Colin_Kaepernick", 5], ["Colin_Kaepernick", 6], ["Colin_Kaepernick", 7], ["Colin_Kaepernick", 8], ["Colin_Kaepernick", 11], ["The_Cry_of_Reason", 0], ["The_Cry_of_Reason", 1], ["Czech_National_Football_League", 0], ["Czech_National_Football_League", 1], ["Czech_National_Football_League", 2], ["Czech_National_Football_League", 3], ["Czech_National_Football_League", 6], ["Czech_National_Football_League", 7], ["Czech_National_Football_League", 8]]} -{"id": 194462, "claim": "Tilda Swinton is a vegan.", "predicted_pages": ["We_Need_to_Talk_About_Kevin_-LRB-film-RRB-", "Snowpiercer", "Tilda", "Michael_Clayton_-LRB-film-RRB-", "Swinton_-LRB-surname-RRB-"], "predicted_sentences": [["Tilda", 12], ["Snowpiercer", 5], ["Swinton_-LRB-surname-RRB-", 19], ["We_Need_to_Talk_About_Kevin_-LRB-film-RRB-", 4], ["Michael_Clayton_-LRB-film-RRB-", 1]], "predicted_pages_ner": ["Tilda_Swinton"], "predicted_sentences_ner": [["Tilda_Swinton", 0], ["Tilda_Swinton", 1], ["Tilda_Swinton", 4], ["Tilda_Swinton", 5], ["Tilda_Swinton", 6], ["Tilda_Swinton", 9], ["Tilda_Swinton", 10], ["Tilda_Swinton", 11], ["Tilda_Swinton", 14], ["Tilda_Swinton", 15], ["Tilda_Swinton", 16], ["Tilda_Swinton", 17], ["Tilda_Swinton", 20], ["Tilda_Swinton", 21]]} -{"id": 137334, "claim": "Fox 2000 Pictures released the film Soul Food.", "predicted_pages": ["John_C._Kilkenny", "Soul_Food", "Maxine_Chadway", "Soul_Food_-LRB-film-RRB-", "Ramona_and_Beezus"], "predicted_sentences": [["Ramona_and_Beezus", 4], ["Soul_Food_-LRB-film-RRB-", 0], ["John_C._Kilkenny", 1], ["Maxine_Chadway", 1], ["Soul_Food", 7]], "predicted_pages_ner": ["Go_Fish_Pictures", "Soul_Food"], "predicted_sentences_ner": [["Go_Fish_Pictures", 0], ["Go_Fish_Pictures", 1], ["Go_Fish_Pictures", 2], ["Go_Fish_Pictures", 5], ["Soul_Food", 0], ["Soul_Food", 3], ["Soul_Food", 5], ["Soul_Food", 7], ["Soul_Food", 9], ["Soul_Food", 11], ["Soul_Food", 13], ["Soul_Food", 15], ["Soul_Food", 17], ["Soul_Food", 19]]} -{"id": 166626, "claim": "Anne Rice was born in New Jersey.", "predicted_pages": ["List_of_Tree_Cities_USA", "Ramses_the_Damned-COLON-_The_Passion_of_Cleopatra", "Nathaniel_Milljour", "Anne_Rice"], "predicted_sentences": [["Nathaniel_Milljour", 15], ["Anne_Rice", 0], ["Ramses_the_Damned-COLON-_The_Passion_of_Cleopatra", 0], ["Ramses_the_Damned-COLON-_The_Passion_of_Cleopatra", 1], ["List_of_Tree_Cities_USA", 1436]], "predicted_pages_ner": ["Anne_Rice", "New_Jersey"], "predicted_sentences_ner": [["Anne_Rice", 0], ["Anne_Rice", 1], ["Anne_Rice", 2], ["Anne_Rice", 5], ["Anne_Rice", 6], ["Anne_Rice", 7], ["Anne_Rice", 8], ["Anne_Rice", 9], ["Anne_Rice", 10], ["Anne_Rice", 13], ["Anne_Rice", 14], ["Anne_Rice", 15], ["Anne_Rice", 16], ["Anne_Rice", 17], ["Anne_Rice", 20], ["Anne_Rice", 21], ["Anne_Rice", 22], ["New_Jersey", 0], ["New_Jersey", 1], ["New_Jersey", 2], ["New_Jersey", 3], ["New_Jersey", 6], ["New_Jersey", 7], ["New_Jersey", 8], ["New_Jersey", 9], ["New_Jersey", 12], ["New_Jersey", 13], ["New_Jersey", 14]]} -{"id": 111897, "claim": "Telemundo is a English-language television network.", "predicted_pages": ["Noticiero_Telemundo", "Noticias_Telemundo", "KTMO-LP", "Decisiones"], "predicted_sentences": [["Noticiero_Telemundo", 0], ["KTMO-LP", 0], ["Noticias_Telemundo", 0], ["Noticias_Telemundo", 8], ["Decisiones", 0]], "predicted_pages_ner": ["Telemundo", "English"], "predicted_sentences_ner": [["Telemundo", 0], ["Telemundo", 1], ["Telemundo", 4], ["Telemundo", 5], ["Telemundo", 8], ["Telemundo", 9], ["Telemundo", 10], ["English", 0], ["English", 3], ["English", 5], ["English", 7], ["English", 9], ["English", 11], ["English", 13], ["English", 15]]} -{"id": 89891, "claim": "Damon Albarn's debut album was released in 2011.", "predicted_pages": ["The_Good,_the_Bad_&_the_Queen_-LRB-song-RRB-", "Damon_Albarn", "Jeff_Wootton"], "predicted_sentences": [["The_Good,_the_Bad_&_the_Queen_-LRB-song-RRB-", 0], ["Damon_Albarn", 4], ["Damon_Albarn", 12], ["Damon_Albarn", 17], ["Jeff_Wootton", 1]], "predicted_pages_ner": ["Damon_Albarn", "2011"], "predicted_sentences_ner": [["Damon_Albarn", 0], ["Damon_Albarn", 1], ["Damon_Albarn", 4], ["Damon_Albarn", 5], ["Damon_Albarn", 6], ["Damon_Albarn", 7], ["Damon_Albarn", 8], ["Damon_Albarn", 11], ["Damon_Albarn", 12], ["Damon_Albarn", 13], ["Damon_Albarn", 14], ["Damon_Albarn", 15], ["Damon_Albarn", 16], ["Damon_Albarn", 17], ["Damon_Albarn", 20], ["Damon_Albarn", 21], ["Damon_Albarn", 22], ["2011", 0], ["2011", 2], ["2011", 4], ["2011", 6]]} -{"id": 181634, "claim": "There is a capital called Mogadishu.", "predicted_pages": ["Mogadishu_-LRB-disambiguation-RRB-", "Battle_of_Mogadishu_-LRB-2009-RRB-", "Mogadishu", "Mogadishu_under_Italian_rule"], "predicted_sentences": [["Mogadishu_-LRB-disambiguation-RRB-", 12], ["Battle_of_Mogadishu_-LRB-2009-RRB-", 0], ["Mogadishu_-LRB-disambiguation-RRB-", 0], ["Mogadishu", 17], ["Mogadishu_under_Italian_rule", 16]], "predicted_pages_ner": ["Mogadishu"], "predicted_sentences_ner": [["Mogadishu", 0], ["Mogadishu", 1], ["Mogadishu", 2], ["Mogadishu", 5], ["Mogadishu", 6], ["Mogadishu", 7], ["Mogadishu", 8], ["Mogadishu", 9], ["Mogadishu", 10], ["Mogadishu", 11], ["Mogadishu", 12], ["Mogadishu", 13], ["Mogadishu", 14], ["Mogadishu", 17], ["Mogadishu", 18], ["Mogadishu", 19], ["Mogadishu", 20], ["Mogadishu", 21], ["Mogadishu", 22], ["Mogadishu", 23], ["Mogadishu", 24], ["Mogadishu", 25], ["Mogadishu", 26], ["Mogadishu", 27], ["Mogadishu", 30], ["Mogadishu", 31], ["Mogadishu", 32], ["Mogadishu", 33], ["Mogadishu", 34], ["Mogadishu", 35], ["Mogadishu", 36], ["Mogadishu", 37], ["Mogadishu", 38]]} -{"id": 219028, "claim": "Savages was exclusively a German film.", "predicted_pages": ["Cultural_Amnesia", "Classic_Incantations-COLON-_The_German_Film_Orchestra_Babelsberg_performs_A.R._Rahman", "My_Leopold", "These_Hopeless_Savages"], "predicted_sentences": [["Classic_Incantations-COLON-_The_German_Film_Orchestra_Babelsberg_performs_A.R._Rahman", 0], ["Cultural_Amnesia", 11], ["These_Hopeless_Savages", 0], ["My_Leopold", 12], ["My_Leopold", 4]], "predicted_pages_ner": ["German"], "predicted_sentences_ner": [["German", 0], ["German", 3], ["German", 5], ["German", 7], ["German", 9], ["German", 11], ["German", 12], ["German", 14], ["German", 16]]} -{"id": 194372, "claim": "Happiness in Slavery is a gospel song by Nine Inch Nails.", "predicted_pages": ["Nine_Inch_Nails_discography", "List_of_awards_and_nominations_received_by_Nine_Inch_Nails", "Nine_Inch_Nails", "List_of_Nine_Inch_Nails_band_members"], "predicted_sentences": [["Nine_Inch_Nails", 16], ["List_of_awards_and_nominations_received_by_Nine_Inch_Nails", 2], ["Nine_Inch_Nails_discography", 8], ["List_of_Nine_Inch_Nails_band_members", 1], ["Nine_Inch_Nails_discography", 4]], "predicted_pages_ner": ["Nine_Inch_Nails"], "predicted_sentences_ner": [["Nine_Inch_Nails", 0], ["Nine_Inch_Nails", 1], ["Nine_Inch_Nails", 4], ["Nine_Inch_Nails", 5], ["Nine_Inch_Nails", 6], ["Nine_Inch_Nails", 9], ["Nine_Inch_Nails", 10], ["Nine_Inch_Nails", 11], ["Nine_Inch_Nails", 12], ["Nine_Inch_Nails", 13], ["Nine_Inch_Nails", 16], ["Nine_Inch_Nails", 17], ["Nine_Inch_Nails", 18], ["Nine_Inch_Nails", 19], ["Nine_Inch_Nails", 20]]} -{"id": 108281, "claim": "Andrew Kevin Walker is only Chinese.", "predicted_pages": ["Andrew_Walker", "Seven_-LRB-1995_film-RRB-", "Sleepy_Hollow_-LRB-film-RRB-", "The_Wolfman_-LRB-2010_film-RRB-", "Nerdland"], "predicted_sentences": [["Sleepy_Hollow_-LRB-film-RRB-", 5], ["Andrew_Walker", 19], ["The_Wolfman_-LRB-2010_film-RRB-", 1], ["Nerdland", 0], ["Seven_-LRB-1995_film-RRB-", 1]], "predicted_pages_ner": ["Andrew_Kevin_Walker", "Chinese"], "predicted_sentences_ner": [["Andrew_Kevin_Walker", 0], ["Andrew_Kevin_Walker", 1], ["Chinese", 0], ["Chinese", 2], ["Chinese", 4], ["Chinese", 6], ["Chinese", 8], ["Chinese", 10], ["Chinese", 12], ["Chinese", 14], ["Chinese", 16], ["Chinese", 18], ["Chinese", 20], ["Chinese", 22], ["Chinese", 24], ["Chinese", 26], ["Chinese", 28], ["Chinese", 30], ["Chinese", 32]]} +{"id": 91198, "claim": "Colin Kaepernick became a starting quarterback during the 49ers 63rd season in the National Football League.", "predicted_pages": ["49ers–Seahawks_rivalry", "Colin_Kaepernick", 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Follows.", "predicted_pages": ["Saw_VI", "Virgin_Rail_Group", "Saw_III"], "predicted_sentences": [["Saw_III", 5], ["Saw_VI", 1], ["Saw_III", 1], ["Virgin_Rail_Group", 6], ["Saw_VI", 11]], "predicted_pages_ner": [], "predicted_sentences_ner": []} +{"id": 87913, "claim": "There are no effects from Global warming.", "predicted_pages": ["Global_warming_controversy", "Global_warming"], "predicted_sentences": [["Global_warming", 19], ["Global_warming_controversy", 0], ["Global_warming", 26], ["Global_warming_controversy", 16], ["Global_warming", 0]], "predicted_pages_ner": [], "predicted_sentences_ner": []} +{"id": 99986, "claim": "Diana, Princess of Wales died in a vehicle accident in Paris on August 31st, 1997.", "predicted_pages": ["Diana,_Princess_of_Wales", "Karpal_Singh"], "predicted_sentences": [["Diana,_Princess_of_Wales", 0], ["Karpal_Singh", 24], ["Karpal_Singh", 20], ["Diana,_Princess_of_Wales", 12], ["Diana,_Princess_of_Wales", 10], ["Diana", 0], ["Diana", 2], ["Diana", 4], 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["Atlas_-LRB-Parkway_Drive_album-RRB-", 1]]} +{"id": 44664, "claim": "NRG Recording Studios was created by a millionaire record producer, engineer and mixer.", "predicted_pages": ["NRG", "Dave_Jerden", "NRG_Recording_Studios", "Neve_8078"], "predicted_sentences": [["NRG_Recording_Studios", 0], ["Dave_Jerden", 0], ["NRG", 27], ["Neve_8078", 0], ["Neve_8078", 35]]} +{"id": 66495, "claim": "Reign Over Me stars people.", "predicted_pages": ["The_Bookworm_-LRB-Bookstore-RRB-", "Bookworm_Adventures"], "predicted_sentences": [["The_Bookworm_-LRB-Bookstore-RRB-", 9], ["Bookworm_Adventures", 1], ["Bookworm_Adventures", 6], ["The_Bookworm_-LRB-Bookstore-RRB-", 5], ["Bookworm_Adventures", 0]]} +{"id": 57243, "claim": "Andrew Kevin Walker is a horror screenwriter.", "predicted_pages": ["Andrew_Walker", "Andrew_Kevin_Walker", "Seven_-LRB-1995_film-RRB-", "Event_Horizon_-LRB-film-RRB-", "Sleepy_Hollow_-LRB-film-RRB-"], "predicted_sentences": [["Andrew_Walker", 19], ["Andrew_Kevin_Walker", 0], ["Sleepy_Hollow_-LRB-film-RRB-", 5], ["Event_Horizon_-LRB-film-RRB-", 1], ["Seven_-LRB-1995_film-RRB-", 1]]} +{"id": 202035, "claim": "Tamerlan Tsarnaev was declared a suspect of a bombing by the CIA.", "predicted_pages": ["2011_Waltham_triple_murder", "Boston_Marathon_bombing", "Tamerlan_Tsarnaev"], "predicted_sentences": [["2011_Waltham_triple_murder", 7], ["Tamerlan_Tsarnaev", 8], ["2011_Waltham_triple_murder", 8], ["Boston_Marathon_bombing", 5], ["Boston_Marathon_bombing", 3]]} +{"id": 5397, "claim": "Miranda Otto began her acting career at age 18.", "predicted_pages": ["Dina_Shihabi", "Miranda_Otto", "In_the_Winter_Dark_-LRB-film-RRB-"], "predicted_sentences": [["Miranda_Otto", 1], ["Dina_Shihabi", 12], ["Dina_Shihabi", 9], ["Miranda_Otto", 0], ["In_the_Winter_Dark_-LRB-film-RRB-", 4]]} +{"id": 46064, "claim": "The NAACP Image Award for Outstanding Supporting Actor in a Drama Series was first given in 1996.", "predicted_pages": ["Outstanding_Drama_Series", "NAACP_Image_Award_for_Outstanding_Supporting_Actor_in_a_Drama_Series", "How_to_Get_Away_with_Murder", "Dev_Patel"], "predicted_sentences": [["Dev_Patel", 6], ["NAACP_Image_Award_for_Outstanding_Supporting_Actor_in_a_Drama_Series", 0], ["Outstanding_Drama_Series", 11], ["NAACP_Image_Award_for_Outstanding_Supporting_Actor_in_a_Drama_Series", 1], ["How_to_Get_Away_with_Murder", 12]]} +{"id": 113783, "claim": "Heavy Metal music was first developed in the United Kingdom.", "predicted_pages": ["Canadian_heavy_metal", "Heavy_metal_lyrics", "Christian_metal", "Andrew_Haug", "List_of_gothic_metal_bands"], "predicted_sentences": [["Canadian_heavy_metal", 4], ["Christian_metal", 15], ["Heavy_metal_lyrics", 0], ["Andrew_Haug", 5], ["List_of_gothic_metal_bands", 4]]} +{"id": 55560, "claim": "Bethany Hamilton was a philosopher.", "predicted_pages": ["Faith_Fay", "California_Surf_Museum", "List_of_homeschooled_people"], "predicted_sentences": [["List_of_homeschooled_people", 137], ["California_Surf_Museum", 9], ["Faith_Fay", 10], ["List_of_homeschooled_people", 78], ["California_Surf_Museum", 10]]} +{"id": 33414, "claim": "Mike Huckabee hosts Oprah.", "predicted_pages": ["David_Huckabee", "Mike_Huckabee_presidential_campaign,_2008", "West_Virginia_Republican_caucuses_and_primary,_2008", "Huckabee_-LRB-disambiguation-RRB-"], "predicted_sentences": [["Mike_Huckabee_presidential_campaign,_2008", 0], ["David_Huckabee", 1], ["David_Huckabee", 2], ["Huckabee_-LRB-disambiguation-RRB-", 9], ["West_Virginia_Republican_caucuses_and_primary,_2008", 13]]} +{"id": 208436, "claim": "Excuse My French is a studio EP.", "predicted_pages": ["Haltya", "Excuse_My_French", "Dropout_Year"], "predicted_sentences": [["Dropout_Year", 25], ["Haltya", 11], ["Dropout_Year", 6], ["Haltya", 16], ["Excuse_My_French", 3]]} +{"id": 75591, "claim": "Shinji Mikami reviewed Resident Evil.", "predicted_pages": ["Resident_Evil_4", "Resident_Evil_-LRB-2002_video_game-RRB-", "Resident_Evil_2", "Jill_Valentine", "Resident_Evil_\u2013_Code-COLON-_Veronica"], "predicted_sentences": [["Resident_Evil_2", 8], ["Jill_Valentine", 6], ["Resident_Evil_-LRB-2002_video_game-RRB-", 7], ["Resident_Evil_4", 7], ["Resident_Evil_\u2013_Code-COLON-_Veronica", 8]]} \ No newline at end of file diff --git a/generate_rte_preds_1.py b/generate_rte_preds_1.py new file mode 100644 index 00000000..6e16f1ca --- /dev/null +++ b/generate_rte_preds_1.py @@ -0,0 +1,145 @@ +import jsonlines +import json +import doc_retrieval +import sentence_retrieval +import rte.rte as rte +import utilities +import spacy +import os +import codecs +import unicodedata as ud +import gensim +from openie import StanfordOpenIE + +from allennlp.models.archival import load_archive +from allennlp.predictors import Predictor + +relevant_sentences_file = "data/dev_concatenation.jsonl" +concatenate_file = "data/dev_concatenation_oie_1.jsonl" +instances = [] +zero_results = 0 +INCLUDE_NER = False +INLCUDE_OIE = True + +relevant_sentences_file = jsonlines.open(relevant_sentences_file) +model = "rte/fever_output/model.tar.gz" +model = load_archive(model) +predictor = Predictor.from_archive(model) + +wiki_dir = "data/wiki-pages/wiki-pages" +wiki_split_docs_dir = "data/wiki-pages-split" + +claim_num = 1 + +wiki_entities = os.listdir(wiki_split_docs_dir) +for i in range(len(wiki_entities)): + wiki_entities[i] = wiki_entities[i].replace("-SLH-", "/") + wiki_entities[i] = wiki_entities[i].replace("_", " ") + wiki_entities[i] = wiki_entities[i][:-5] + wiki_entities[i] = wiki_entities[i].replace("-LRB-", "(") + wiki_entities[i] = wiki_entities[i].replace("-RRB-", ")") + # tokens_sentence = gensim.utils.simple_preprocess(wiki_entities[i]) + # wiki_entities[i] = ' '.join(map(str, tokens_sentence)) + +print("Wiki entities successfully parsed") + +for line in relevant_sentences_file: + instances.append(line) + +nlp = spacy.load('en_core_web_lg') + + +def create_test_set(claim, candidateEvidences, claim_num): + testset = [] + for elem in candidateEvidences: + testset.append({"hypothesis": claim, "premise": elem}) + return testset + + +def run_rte(claim, evidence, claim_num): + fname = "claim_" + str(claim_num) + ".json" + test_set = create_test_set(claim, evidence, claim_num) + preds = predictor.predict_batch_json(test_set) + return preds + + +with StanfordOpenIE() as client: + with jsonlines.open(concatenate_file, mode='w') as writer_c: + for i in range(0, 2500): + claim = instances[i]['claim'] + print(claim) + evidence = instances[i]['predicted_sentences'] + potential_evidence_sentences = [] + + for sentence in evidence: + # print(sentence) + # print(sentence[0]) + # load document from TF-IDF + relevant_doc = ud.normalize('NFC', sentence[0]) + relevant_doc = relevant_doc.replace("/", "-SLH-") + file = codecs.open(wiki_split_docs_dir + "/" + relevant_doc + ".json", "r", "utf-8") + file = json.load(file) + full_lines = file["lines"] + + lines = [] + for line in full_lines: + lines.append(line['content']) + + lines[sentence[1]] = lines[sentence[1]].strip() + lines[sentence[1]] = lines[sentence[1]].replace("-LRB-", " ( ") + lines[sentence[1]] = lines[sentence[1]].replace("-RRB-", " ) ") + + potential_evidence_sentences.append(lines[sentence[1]]) + + # Just adding a check + # This is needed in case nothing was predicted + if len(potential_evidence_sentences) == 0: + zero_results += 1 + potential_evidence_sentences.append("Nothing") + evidence.append(["Nothing", 0]) + + # this will create document retrieval and sentence retrieval based on NER + if INCLUDE_NER: + relevant_docs, entities = doc_retrieval.getRelevantDocs(claim, wiki_entities, "spaCy", + nlp) # "spaCy", nlp)# + print(relevant_docs) + # print(entities) + relevant_sentences = sentence_retrieval.getRelevantSentences(relevant_docs, entities, wiki_split_docs_dir) + # print(relevant_sentences) + + predicted_evidence = [] + for sent in relevant_sentences: + predicted_evidence.append((sent['id'], sent['line_num'])) + potential_evidence_sentences.append(sent['sentence']) + evidence.append((sent['id'], sent['line_num'])) + + instances[i]['predicted_pages_ner'] = relevant_docs + instances[i]['predicted_sentences_ner'] = predicted_evidence + + preds = run_rte(claim, potential_evidence_sentences, claim_num) + + saveFile = codecs.open("rte/entailment_predictions/claim_" + str(claim_num) + ".json", mode="w+", + encoding="utf-8") + for j in range(len(preds)): + # print(preds) + # print(evidence) + preds[j]['claim'] = claim + preds[j]['premise_source_doc_id'] = evidence[j][0] + preds[j]['premise_source_doc_line_num'] = evidence[j][1] + preds[j]['premise_source_doc_sentence'] = potential_evidence_sentences[j] + saveFile.write(json.dumps(preds[j], ensure_ascii=False) + "\n") + + saveFile.close() + claim_num += 1 + # print(claim_num) + # print(instances[i]) + + if INLCUDE_OIE: + relevant_docs, entities = doc_retrieval.get_docs_with_oie(claim, wiki_entities, client) + print(entities) + instances[i]['predicted_pages_oie'] = relevant_docs + + writer_c.write(instances[i]) + print("Claim number: " + str(i) + " of " + str(len(instances))) + +print("Number of Zero Sentences Found: " + str(zero_results)) diff --git a/generate_rte_preds_2.py b/generate_rte_preds_2.py new file mode 100644 index 00000000..f25075aa --- /dev/null +++ b/generate_rte_preds_2.py @@ -0,0 +1,145 @@ +import jsonlines +import json +import doc_retrieval +import sentence_retrieval +import rte.rte as rte +import utilities +import spacy +import os +import codecs +import unicodedata as ud +import gensim +from openie import StanfordOpenIE + +from allennlp.models.archival import load_archive +from allennlp.predictors import Predictor + +relevant_sentences_file = "data/dev_concatenation.jsonl" +concatenate_file = "data/dev_concatenation_oie_2.jsonl" +instances = [] +zero_results = 0 +INCLUDE_NER = False +INLCUDE_OIE = True + +relevant_sentences_file = jsonlines.open(relevant_sentences_file) +model = "rte/fever_output/model.tar.gz" +model = load_archive(model) +predictor = Predictor.from_archive(model) + +wiki_dir = "data/wiki-pages/wiki-pages" +wiki_split_docs_dir = "data/wiki-pages-split" + +claim_num = 1 + +wiki_entities = os.listdir(wiki_split_docs_dir) +for i in range(len(wiki_entities)): + wiki_entities[i] = wiki_entities[i].replace("-SLH-", "/") + wiki_entities[i] = wiki_entities[i].replace("_", " ") + wiki_entities[i] = wiki_entities[i][:-5] + wiki_entities[i] = wiki_entities[i].replace("-LRB-", "(") + wiki_entities[i] = wiki_entities[i].replace("-RRB-", ")") + # tokens_sentence = gensim.utils.simple_preprocess(wiki_entities[i]) + # wiki_entities[i] = ' '.join(map(str, tokens_sentence)) + +print("Wiki entities successfully parsed") + +for line in relevant_sentences_file: + instances.append(line) + +nlp = spacy.load('en_core_web_lg') + + +def create_test_set(claim, candidateEvidences, claim_num): + testset = [] + for elem in candidateEvidences: + testset.append({"hypothesis": claim, "premise": elem}) + return testset + + +def run_rte(claim, evidence, claim_num): + fname = "claim_" + str(claim_num) + ".json" + test_set = create_test_set(claim, evidence, claim_num) + preds = predictor.predict_batch_json(test_set) + return preds + + +with StanfordOpenIE() as client: + with jsonlines.open(concatenate_file, mode='w') as writer_c: + for i in range(2500, 5000): + claim = instances[i]['claim'] + print(claim) + evidence = instances[i]['predicted_sentences'] + potential_evidence_sentences = [] + + for sentence in evidence: + # print(sentence) + # print(sentence[0]) + # load document from TF-IDF + relevant_doc = ud.normalize('NFC', sentence[0]) + relevant_doc = relevant_doc.replace("/", "-SLH-") + file = codecs.open(wiki_split_docs_dir + "/" + relevant_doc + ".json", "r", "utf-8") + file = json.load(file) + full_lines = file["lines"] + + lines = [] + for line in full_lines: + lines.append(line['content']) + + lines[sentence[1]] = lines[sentence[1]].strip() + lines[sentence[1]] = lines[sentence[1]].replace("-LRB-", " ( ") + lines[sentence[1]] = lines[sentence[1]].replace("-RRB-", " ) ") + + potential_evidence_sentences.append(lines[sentence[1]]) + + # Just adding a check + # This is needed in case nothing was predicted + if len(potential_evidence_sentences) == 0: + zero_results += 1 + potential_evidence_sentences.append("Nothing") + evidence.append(["Nothing", 0]) + + # this will create document retrieval and sentence retrieval based on NER + if INCLUDE_NER: + relevant_docs, entities = doc_retrieval.getRelevantDocs(claim, wiki_entities, "spaCy", + nlp) # "spaCy", nlp)# + print(relevant_docs) + # print(entities) + relevant_sentences = sentence_retrieval.getRelevantSentences(relevant_docs, entities, wiki_split_docs_dir) + # print(relevant_sentences) + + predicted_evidence = [] + for sent in relevant_sentences: + predicted_evidence.append((sent['id'], sent['line_num'])) + potential_evidence_sentences.append(sent['sentence']) + evidence.append((sent['id'], sent['line_num'])) + + instances[i]['predicted_pages_ner'] = relevant_docs + instances[i]['predicted_sentences_ner'] = predicted_evidence + + preds = run_rte(claim, potential_evidence_sentences, claim_num) + + saveFile = codecs.open("rte/entailment_predictions/claim_" + str(claim_num) + ".json", mode="w+", + encoding="utf-8") + for j in range(len(preds)): + # print(preds) + # print(evidence) + preds[j]['claim'] = claim + preds[j]['premise_source_doc_id'] = evidence[j][0] + preds[j]['premise_source_doc_line_num'] = evidence[j][1] + preds[j]['premise_source_doc_sentence'] = potential_evidence_sentences[j] + saveFile.write(json.dumps(preds[j], ensure_ascii=False) + "\n") + + saveFile.close() + claim_num += 1 + # print(claim_num) + # print(instances[i]) + + if INLCUDE_OIE: + relevant_docs, entities = doc_retrieval.get_docs_with_oie(claim, wiki_entities, client) + print(entities) + instances[i]['predicted_pages_oie'] = relevant_docs + + writer_c.write(instances[i]) + print("Claim number: " + str(i) + " of " + str(len(instances))) + +print("Number of Zero Sentences Found: " + str(zero_results)) diff --git a/generate_rte_preds_3.py b/generate_rte_preds_3.py new file mode 100644 index 00000000..b1b66721 --- /dev/null +++ b/generate_rte_preds_3.py @@ -0,0 +1,145 @@ +import jsonlines +import json +import doc_retrieval +import sentence_retrieval +import rte.rte as rte +import utilities +import spacy +import os +import codecs +import unicodedata as ud +import gensim +from openie import StanfordOpenIE + +from allennlp.models.archival import load_archive +from allennlp.predictors import Predictor + +relevant_sentences_file = "data/dev_concatenation.jsonl" +concatenate_file = "data/dev_concatenation_oie_3.jsonl" +instances = [] +zero_results = 0 +INCLUDE_NER = False +INLCUDE_OIE = True + +relevant_sentences_file = jsonlines.open(relevant_sentences_file) +model = "rte/fever_output/model.tar.gz" +model = load_archive(model) +predictor = Predictor.from_archive(model) + +wiki_dir = "data/wiki-pages/wiki-pages" +wiki_split_docs_dir = "data/wiki-pages-split" + +claim_num = 1 + +wiki_entities = os.listdir(wiki_split_docs_dir) +for i in range(len(wiki_entities)): + wiki_entities[i] = wiki_entities[i].replace("-SLH-", "/") + wiki_entities[i] = wiki_entities[i].replace("_", " ") + wiki_entities[i] = wiki_entities[i][:-5] + wiki_entities[i] = wiki_entities[i].replace("-LRB-", "(") + wiki_entities[i] = wiki_entities[i].replace("-RRB-", ")") + # tokens_sentence = gensim.utils.simple_preprocess(wiki_entities[i]) + # wiki_entities[i] = ' '.join(map(str, tokens_sentence)) + +print("Wiki entities successfully parsed") + +for line in relevant_sentences_file: + instances.append(line) + +nlp = spacy.load('en_core_web_lg') + + +def create_test_set(claim, candidateEvidences, claim_num): + testset = [] + for elem in candidateEvidences: + testset.append({"hypothesis": claim, "premise": elem}) + return testset + + +def run_rte(claim, evidence, claim_num): + fname = "claim_" + str(claim_num) + ".json" + test_set = create_test_set(claim, evidence, claim_num) + preds = predictor.predict_batch_json(test_set) + return preds + + +with StanfordOpenIE() as client: + with jsonlines.open(concatenate_file, mode='w') as writer_c: + for i in range(5000, 7500): + claim = instances[i]['claim'] + print(claim) + evidence = instances[i]['predicted_sentences'] + potential_evidence_sentences = [] + + for sentence in evidence: + # print(sentence) + # print(sentence[0]) + # load document from TF-IDF + relevant_doc = ud.normalize('NFC', sentence[0]) + relevant_doc = relevant_doc.replace("/", "-SLH-") + file = codecs.open(wiki_split_docs_dir + "/" + relevant_doc + ".json", "r", "utf-8") + file = json.load(file) + full_lines = file["lines"] + + lines = [] + for line in full_lines: + lines.append(line['content']) + + lines[sentence[1]] = lines[sentence[1]].strip() + lines[sentence[1]] = lines[sentence[1]].replace("-LRB-", " ( ") + lines[sentence[1]] = lines[sentence[1]].replace("-RRB-", " ) ") + + potential_evidence_sentences.append(lines[sentence[1]]) + + # Just adding a check + # This is needed in case nothing was predicted + if len(potential_evidence_sentences) == 0: + zero_results += 1 + potential_evidence_sentences.append("Nothing") + evidence.append(["Nothing", 0]) + + # this will create document retrieval and sentence retrieval based on NER + if INCLUDE_NER: + relevant_docs, entities = doc_retrieval.getRelevantDocs(claim, wiki_entities, "spaCy", + nlp) # "spaCy", nlp)# + print(relevant_docs) + # print(entities) + relevant_sentences = sentence_retrieval.getRelevantSentences(relevant_docs, entities, wiki_split_docs_dir) + # print(relevant_sentences) + + predicted_evidence = [] + for sent in relevant_sentences: + predicted_evidence.append((sent['id'], sent['line_num'])) + potential_evidence_sentences.append(sent['sentence']) + evidence.append((sent['id'], sent['line_num'])) + + instances[i]['predicted_pages_ner'] = relevant_docs + instances[i]['predicted_sentences_ner'] = predicted_evidence + + preds = run_rte(claim, potential_evidence_sentences, claim_num) + + saveFile = codecs.open("rte/entailment_predictions/claim_" + str(claim_num) + ".json", mode="w+", + encoding="utf-8") + for j in range(len(preds)): + # print(preds) + # print(evidence) + preds[j]['claim'] = claim + preds[j]['premise_source_doc_id'] = evidence[j][0] + preds[j]['premise_source_doc_line_num'] = evidence[j][1] + preds[j]['premise_source_doc_sentence'] = potential_evidence_sentences[j] + saveFile.write(json.dumps(preds[j], ensure_ascii=False) + "\n") + + saveFile.close() + claim_num += 1 + # print(claim_num) + # print(instances[i]) + + if INLCUDE_OIE: + relevant_docs, entities = doc_retrieval.get_docs_with_oie(claim, wiki_entities, client) + print(entities) + instances[i]['predicted_pages_oie'] = relevant_docs + + writer_c.write(instances[i]) + print("Claim number: " + str(i) + " of " + str(len(instances))) + +print("Number of Zero Sentences Found: " + str(zero_results)) diff --git a/generate_rte_preds_4.py b/generate_rte_preds_4.py new file mode 100644 index 00000000..1aa61dff --- /dev/null +++ b/generate_rte_preds_4.py @@ -0,0 +1,145 @@ +import jsonlines +import json +import doc_retrieval +import sentence_retrieval +import rte.rte as rte +import utilities +import spacy +import os +import codecs +import unicodedata as ud +import gensim +from openie import StanfordOpenIE + +from allennlp.models.archival import load_archive +from allennlp.predictors import Predictor + +relevant_sentences_file = "data/dev_concatenation.jsonl" +concatenate_file = "data/dev_concatenation_oie_4.jsonl" +instances = [] +zero_results = 0 +INCLUDE_NER = False +INLCUDE_OIE = True + +relevant_sentences_file = jsonlines.open(relevant_sentences_file) +model = "rte/fever_output/model.tar.gz" +model = load_archive(model) +predictor = Predictor.from_archive(model) + +wiki_dir = "data/wiki-pages/wiki-pages" +wiki_split_docs_dir = "data/wiki-pages-split" + +claim_num = 1 + +wiki_entities = os.listdir(wiki_split_docs_dir) +for i in range(len(wiki_entities)): + wiki_entities[i] = wiki_entities[i].replace("-SLH-", "/") + wiki_entities[i] = wiki_entities[i].replace("_", " ") + wiki_entities[i] = wiki_entities[i][:-5] + wiki_entities[i] = wiki_entities[i].replace("-LRB-", "(") + wiki_entities[i] = wiki_entities[i].replace("-RRB-", ")") + # tokens_sentence = gensim.utils.simple_preprocess(wiki_entities[i]) + # wiki_entities[i] = ' '.join(map(str, tokens_sentence)) + +print("Wiki entities successfully parsed") + +for line in relevant_sentences_file: + instances.append(line) + +nlp = spacy.load('en_core_web_lg') + + +def create_test_set(claim, candidateEvidences, claim_num): + testset = [] + for elem in candidateEvidences: + testset.append({"hypothesis": claim, "premise": elem}) + return testset + + +def run_rte(claim, evidence, claim_num): + fname = "claim_" + str(claim_num) + ".json" + test_set = create_test_set(claim, evidence, claim_num) + preds = predictor.predict_batch_json(test_set) + return preds + + +with StanfordOpenIE() as client: + with jsonlines.open(concatenate_file, mode='w') as writer_c: + for i in range(7500, len(instances)): + claim = instances[i]['claim'] + print(claim) + evidence = instances[i]['predicted_sentences'] + potential_evidence_sentences = [] + + for sentence in evidence: + # print(sentence) + # print(sentence[0]) + # load document from TF-IDF + relevant_doc = ud.normalize('NFC', sentence[0]) + relevant_doc = relevant_doc.replace("/", "-SLH-") + file = codecs.open(wiki_split_docs_dir + "/" + relevant_doc + ".json", "r", "utf-8") + file = json.load(file) + full_lines = file["lines"] + + lines = [] + for line in full_lines: + lines.append(line['content']) + + lines[sentence[1]] = lines[sentence[1]].strip() + lines[sentence[1]] = lines[sentence[1]].replace("-LRB-", " ( ") + lines[sentence[1]] = lines[sentence[1]].replace("-RRB-", " ) ") + + potential_evidence_sentences.append(lines[sentence[1]]) + + # Just adding a check + # This is needed in case nothing was predicted + if len(potential_evidence_sentences) == 0: + zero_results += 1 + potential_evidence_sentences.append("Nothing") + evidence.append(["Nothing", 0]) + + # this will create document retrieval and sentence retrieval based on NER + if INCLUDE_NER: + relevant_docs, entities = doc_retrieval.getRelevantDocs(claim, wiki_entities, "spaCy", + nlp) # "spaCy", nlp)# + print(relevant_docs) + # print(entities) + relevant_sentences = sentence_retrieval.getRelevantSentences(relevant_docs, entities, wiki_split_docs_dir) + # print(relevant_sentences) + + predicted_evidence = [] + for sent in relevant_sentences: + predicted_evidence.append((sent['id'], sent['line_num'])) + potential_evidence_sentences.append(sent['sentence']) + evidence.append((sent['id'], sent['line_num'])) + + instances[i]['predicted_pages_ner'] = relevant_docs + instances[i]['predicted_sentences_ner'] = predicted_evidence + + preds = run_rte(claim, potential_evidence_sentences, claim_num) + + saveFile = codecs.open("rte/entailment_predictions/claim_" + str(claim_num) + ".json", mode="w+", + encoding="utf-8") + for j in range(len(preds)): + # print(preds) + # print(evidence) + preds[j]['claim'] = claim + preds[j]['premise_source_doc_id'] = evidence[j][0] + preds[j]['premise_source_doc_line_num'] = evidence[j][1] + preds[j]['premise_source_doc_sentence'] = potential_evidence_sentences[j] + saveFile.write(json.dumps(preds[j], ensure_ascii=False) + "\n") + + saveFile.close() + claim_num += 1 + # print(claim_num) + # print(instances[i]) + + if INLCUDE_OIE: + relevant_docs, entities = doc_retrieval.get_docs_with_oie(claim, wiki_entities, client) + print(entities) + instances[i]['predicted_pages_oie'] = relevant_docs + + writer_c.write(instances[i]) + print("Claim number: " + str(i) + " of " + str(len(instances))) + +print("Number of Zero Sentences Found: " + str(zero_results))