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System Text2Drs takes English narrative as an input and outputs a discourse representation structure in Neo-Davidsonian style. Text2DRS relies on lexical resource VerbNet for annotating the specific relations between relevant entities and events mentioned in the given narrative using the verb classes and thematic roles of VerbNet.

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gling07/Text2DRS

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From Narrative Text to Formal Action Language System Descriptions

What is this repository for?

Text2DRS is written in Python3.

  • System Text2DRS that takes a narrative text file as an input and produces a discourse representation structure (drs) as an output

System setup guideline

[LTH]
Path: <absolute-path-to-LTH>/lth_srl

[CoreNLP]
Path: <absolute-path-to-CoreNLP>/stanford-corenlp-full-2016-10-31

Parameters

  • Command line to invoke the system:
  • python3 text2drs.py /CONFIG.cfg /something.txt

For example, testFiles/paperExample.txt contains two sentences Ann went to the room. Michael left the room.

If this file is an input file of text2drs.py then the output will be placed into text2drsOutputs folder under the name paperExample_drs.txt (See below)

System output

  • the output file is in the text2drsOutputs folder

  • input file name: paperExample.txt

  • drs output file name: paperExample_drs.txt

  • verbnet srl output file name: paperExample_verbNetsrl.txt

  • drs file contents:

% r1, r2, r3, e1, e2
% ============================================================

entity(r1). entity(r2). entity(r3).

property(r1, "Ann"). property(r2, "room"). property(r3, "Michael").

event(e1).
event(e2).

eventType(e1, "51.1"). eventType(e2, "13.3").

eventTime(e1, 0). eventTime(e2, 1).

eventArgument(e1, "Theme", r1). eventArgument(e1, "Destination", r2). eventArgument(e2, "Agent", r3).
eventArgument(e2, "Theme", r2).

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License

  • MIT License

About

System Text2Drs takes English narrative as an input and outputs a discourse representation structure in Neo-Davidsonian style. Text2DRS relies on lexical resource VerbNet for annotating the specific relations between relevant entities and events mentioned in the given narrative using the verb classes and thematic roles of VerbNet.

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