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README.md

README.md

Abduction-based Discource Processing System

In this repository, we store all the resources and tools constituting our Natural Language Understanding system based on Abductive Reasoning implemented for 4 languages:

  • English
  • Spanish
  • Russian
  • Farsi

The system is largely based on ideas summarized in [Hobbs, 1993].

Our abductive Natural Language Understanding pipeline is shown below.

Fig.

Text fragments are given as input to the pipeline. The text fragments are parsed. For Russian and Spanish tagging, we use TreeTagger. For Farsi tagging, we use the Stanford NLP tagger. For parsing, we use the dependency parser Malt for Spanish, Russian, and Farsi. For English, the whole processing is performed by the Boxer semantic parser).

The parses are input to the module converting them into logical forms. A logical form (LF) is a conjunction of propositions, which have generalized eventuality arguments that can be used for showing relationships among the propositions. We use logical representations of natural language texts as described in [Hobbs, 1995]. For Spanish, Russian, and Farsi, we have developed logical form converters. For English, we use the LF converter built in the Boxer semantic parser.

Logical forms and a knowledge base are input to the abductive reasoner based on Integer Linear Programming [Inoue et al., 2012]. The reasoner produces flat first order logic interpretations in the textual format and proof graphs in the PDF format.

More details about each component can be found here.


Installation and running

  • Clone ADP-pipeline repository
git clone https://github.com/eovchinn/ADP-pipeline
  • Install external packages and software; see instructions here

  • Run the system; see instructions here


System requirements

  • Linux or Mac
  • at least 4 cores CPU
  • at least 8GB RAM

Contact


Publications

Inoue, N., E. Ovchinnikova, K. Inui and J. R. Hobbs (2014). Weighted Abduction for Discourse Processing Based on Integer Linear Programming. In Sukthankar, G., Goldman, R. P., Geib, S., Pynadath, D. V., Hung, H. B. (eds.) : Plan, Activity, and Intent Recognition, pp. 33-55. [Link]

Ovchinnikova, E., Israel, R., Wertheim, S., Zaytsev, V., Montazeri, N, and Hobbs, J. (2014). Abductive Inference for Interpretation of Metaphors. In Proc. of ACL 2014 Workshop on Metaphor in NLP, pp. 33--41. [PDF]

Ovchinnikova, E., N. Montazeri, T. Alexandrov, J. R. Hobbs, M. C. McCord and R. Mulkar-Mehta (2014). Abductive Reasoning with a Large Knowledge Base for Discourse Processing. In Hunt, H., Bos, J. and Pulman, S. (eds.) : Computing Meaning, vol. 4, pp. 104-124. [PDF]

Ovchinnikova, E. (2012). Integration of World Knowledge for Natural Language Understanding, Atlantis Press, Springer. [Link]

Inoue, N., E. Ovchinnikova, K. Inui, and J. R. Hobbs (2012). Coreference Resolution with ILP-based Weighted Abduction. In Proc. of COLING'12, pp.1291-1308. [PDF]

Ovchinnikova, E., N. Montazeri, T. Alexandrov, J. R. Hobbs, M. C. McCord and R. Mulkar-Mehta (2011). Abductive Reasoning with a Large Knowledge Base for Discourse Processing. In Proc. the IWCS'11, pp. 225-234. [Link]