Weighted Abduction for Discourse Processing Based on Integer Linear Programming

Naoya Inoue, Ekaterina Ovchinnikova, Kentaro Inui, Jerry Hobbs

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)


This chapter explores the logical framework called weighted abduction as applied to solving discourse-processing tasks. Weighted abduction incorporates a cost propagation mechanism allowing us to estimate the likelihood of the obtained abductive proofs. We use a tractable implementation of weighted abduction based on Integer Linear Programming and a large knowledge base generated automatically. We first perform an experiment on plan recognition using the dataset originally developed for Ng and Mooney's system [39]. Then we apply our discourse processing pipeline for predicting whether one text fragment logically entails another one (Recognizing Textual Entailment task). The study we describe is the first attempt to apply tractable inference-based natural language processing on a large scale.

Original languageEnglish
Title of host publicationPlan, Activity, and Intent Recognition
Subtitle of host publicationTheory and Practice
PublisherElsevier Inc.
Number of pages23
ISBN (Print)9780123985323
Publication statusPublished - 2014 Mar


  • Integer Linear Programming
  • Natural language understanding
  • Planning
  • Recognizing Textual Entailment
  • Weighted abduction

ASJC Scopus subject areas

  • Computer Science(all)


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