From Narrative Text to Formal Action Language System Descriptions

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

• Python 3.6 Note that the version of python is ESSENTIAL
• Download or git clone (https://github.com/gling07/Text2DRS) Text2DRS repository
• If you already have LTH or Stanford core-NLP 3.7.0, you can omit related steps and edit CONFIG file directly
• Download LTH (http://nlp.cs.lth.se/software/semantic-parsing-propbank-nombank-frames/)
• Unzip LTH package
• Download Standford core-NLP 3.7.0 package (https://stanfordnlp.github.io/CoreNLP/history.html)
• Unzip core-NLP package
• Edit CONFIG.cfg file to include system paths of LTH and core-NLP package as following:

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

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

• 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)

• 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).

• Link to bAbl (https://research.fb.com/downloads/babi/)
• Link to VerbNet (https://verbs.colorado.edu/verb-index/)
• Link to SemLink (https://verbs.colorado.edu/semlink/)

• Gang Ling ([email protected])
• Dr. Yuliya Lierler ([email protected])

• MIT License