KENE(Konan English kNowledge Extractor)

This repository contains codes and sample data for English knowledge extraction from Friends captions.

Requirements

• KEMA(Konan English Morphology Analyzer)
• KEPT(Konan English Pos Tagger)
• PCRE library
• GCC

Building

$ make

Input Format

Each Friends caption entry must be given in a single line as following format similar to that of CSV.

<period_num>,<start_time>,<end_time>,<speaker>,<caption>
  - period_num: period number
  - start_time: period start time
  - end_time: period end time
  - speaker: caption speaker
  - caption: English caption

• Example

6,0:01:08,0:01:11,Monica,Okay, everybody relax. This is not even a date.

See the file "dat/input" for more examples.

Output Format

The extracted knowledge is a JSON array consists of caption analysis objects. The object is represented as following format.

 {
  "period_num": "period number", 
  "start_time": "period start time", 
  "end_time": "period end time", 
  "speaker": "caption speaker",
  "caption": "English caption",
  "results": [
   { 
    "pred": "predicate", 
    "args": [ "argument", ... ]
   }
   ...
  ]
 }

• Example

 {
  "period_num": "6",
  "start_time": "0:01:08",
  "end_time": "0:01:11",
  "speaker": "Monica",
  "caption": "Okay, everybody relax. This is not even a date.",
  "results": [
   {
    "pred": "is not",
    "args": [ "This", "even a date" ]
   }
  ]
 }

See the file "dat/output.json" for more examples.

Running

• Usage

kene data-path config-file input-file output-file

'config-file', 'input-file' and 'output-file' must be relative to 'data-path'.

• Example

$ export LD_LIBRARY_PATH=./lib:./lib/pcre:$LD_LIBRARY_PATH
$ ./bin/kene dat kene.rc input output.json

See the file "kene.sh" for the running example.

See the file "dat/kene.rc" for the example of KENE configuration.

Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-01780, The technology development for event recognition/relational reasoning and learning knowledge based system for video understanding)