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nltk-api-server

API server for NLTK. Aimed to provide convenient interface to use NLTK over any programming language

Features

  • Stemming
  • Lemmatization
  • Part of Speech Tagging (Unigram/Bigram/Regex)
  • Named Entity Recognition
  • Sentiment Analysis

1. Stemming

NLTK Stemmers used: Porter, Snowball and Lancaster

  • Accepts:

    • /api/stem?words=<words>/

    • /api/stem?words=<words>&stemmer=<porter/snowball/lancaster/default>/

    • /api/stem?words=<words>&stemmer=snowball&language=<language>&ignore_stopwords=<true/false>/

  • Query Parameters:

    • Mandatory:

      • words:

        value: string comma separated

    • Optional:

      • stemmer:

        value: porter/snowball/lancaster/default

        default: snowball

      • ignore_stopwords: Only for Snowball Stemmer

        value: true/false

        default: false

      • language: Only for Snowball Stemmer

        value: see SnowballStemmer.languages

        default: english

1.1 Examples

  • localhost:9000/api/stem?words=dangerous,monitoring,testing

      {
      "status": true,
      "result": [
          "danger",
          "monitor",
          "test"
      ]
  }

  • localhost:9000/api/stem/?words=dangerous,monitoring,testing&stemmer=snowball

      {
      "status": true,
      "result": [
          "dog",
          "cat",
          "vertex"
      ]
  }

  • The above examples do not cover all cases. See the section above examples for more features

2. Lemmatization

NLTK Lemmatizer used: WordNetLemmatizer

  • Accepts:

    • /api/lemma?words=<words>/

  • Query Parameters:

    • Mandatory:

      • words:

        value: string comma separated

2.1 Examples

  • localhost/api/lemma/?words=dogs,cats,vertices

      {
      "status":true,
      "result": [
          "dog",
          "cat",
          "vertex"
      ]
  }

3. Part of Speech Tagging

NLTK POS tagger used: pos_tag, UnigramTagger, BigramTagger & RegexpTagger

  • Accepts:

    • /api/tag?sentence=<sentence>/

    • /api/tag?sentence=<sentence>&tagger=<pos/unigram/bigram/default>/

    • /api/tag?sentence=<sentence>&tagger=<pos/unigram/bigram/default>&train=<categories>/

    including any query parameter accepted by /api/tag/

  • Query Parameters:

    • Mandatory:

      • sentence:

        value: string

    • Optional:

      • tagger:

        value: pos/unigram/bigram/regex

        default: pos_tag

      • train (iff unigram/bigram):

        value: 'news', 'editorial', 'reviews', 'religion', 'learned', 'science_fiction', 'romance', 'humor'

        default: 'news'

      • any query parameter acceptable by /api/tag/

3.1 Examples

  • localhost/api/sentence=this is a test

      {
      "status": true,
      "result": [
          [
              "this",
              "DT"
          ],
          [
              "is",
              "VBZ"
          ],
          [
              "a",
              "DT"
          ],
          [
              "test",
              "NN"
          ]
      ]
  }

  • localhost/api/sentence=this is a test&tagger=unigram

      {
      "status": true,
      "result": [
          [
              "this",
              "DT"
          ],
          [
              "is",
              "BEZ"
          ],
          [
              "a",
              "AT"
          ],
          [
              "test",
              "NN"
          ]
      ]
  }

  • The above examples do not cover all cases. See the section above examples for more features

  • Remember, we can also use trained data along with the unigram/bigram tagger: 'news', 'editorial', 'reviews', 'religion', 'learned', 'science_fiction', 'romance', 'humor'

4. Named Entity Recognition

NLTK NER used: ne_chunk

  • Accepts:

    • /api/ner?sentence=<sentence>/

    including any query parameter accepted by /api/tag/

  • Query Parameters:

    • Mandatory:

      • sentence:

        value: string

      • Optional:

        • any query parameter acceptable by /api/tag/

4.1 Examples

  • localhost/api/ner?sentence=At the Olympics in August, Phelps picked up five gold medal

      {
      "status": true,
      "result": [
          "Phelps"
      ]
  }

5. Sentiment Analysis

NLTK Sentiment Analyzer used: vader

  • Accepts:

    • /api/sentiment?sentence=<sentence>/

  • Query Parameters:

    • Mandatory:

      • sentence:

        value: string

    • Optional:

      • any query parameter acceptable by /api/tag/

5.1 Examples

  • localhost/api/sentiment?sentence=At the Olympics in August, Phelps picked up five gold medal

      {
      "status": true,
      "result": {
          "neg": 0,
          "neu": 0.256,
          "pos": 0.744,
          "compound": 0.4404
      }
  }

6. Run on local

  • git clone [email protected]:vipul-sharma20/nltk-api-server.git
  • cd nltk-api-server
  • sudo pip install virtualenv
  • virtualenv nltk-api
  • source nltk-api/bin/activate
  • pip install -r requirements.txt (wait till the requirements are installed)
  • python manage.py runserver This will run the application on http://127.0.0.1:8000/

IMPORTANT: You will require some corpora and trained models for the code to run. You can refer to: http://www.nltk.org/data.html

  • Interactive Method:

      In [1]: import nltk

  In [2]: nltk.download()