Weighted Abduction for Discourse Processing Based on Integer Linear Programming

Abstract 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.

[1]  Gene A. Tagliarini,et al.  An Evolutionary Optimization Approach to Cost-Based Abduction, with Comparison to PSO , 2007, 2007 International Joint Conference on Neural Networks.

[2]  Solomon Eyal Shimony,et al.  Probabilistic Semantics for Cost Based Abduction , 1990, AAAI.

[3]  Raymond J. Mooney,et al.  Abductive Markov Logic for Plan Recognition , 2011, Proceedings of the AAAI Conference on Artificial Intelligence.

[4]  David Poole,et al.  Logic programming, abduction and probability , 1993, New Generation Computing.

[5]  Sebastian Riedel,et al.  Incremental Integer Linear Programming for Non-projective Dependency Parsing , 2006, EMNLP.

[6]  Konstantina Garoufi.  Towards a Better Understanding of Applied Textual Entailment: Annotation and Evaluation of the RTE-2 Dataset , 2007 .

[7]  Stefano Borgo,et al.  Data-Driven and Ontological Analysis of FrameNet for Natural Language Reasoning , 2010, LREC.

[8]  Jerry R. Hobbs,et al.  Abductive Reasoning with a Large Knowledge Base for Discourse Processing , 2011, IWCS.

[9]  H. Prendinger,et al.  First-Order Diagnosis by Propositional Reasoning: A Representation-Based Approach , 1999 .

[10]  Kentaro Inui,et al.  ILP-Based Reasoning for Weighted Abduction , 2011, Plan, Activity, and Intent Recognition.

[11]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[12]  Solomon Eyal Shimony,et al.  Cost-Based Abduction and MAP Explanation , 1994, Artif. Intell..

[13]  Peter Clark,et al.  Recognizing Textual Entailment with Logical Inference , 2008, TAC.

[14]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[15]  Ido Dagan,et al.  Global Learning of Typed Entailment Rules , 2011, ACL.

[16]  Raymond J. Mooney,et al.  Abductive Plan Recognition by Extending Bayesian Logic Programs , 2011, ECML/PKDD.

[17]  Inui Kentaro,et al.  Online Large-margin Weight Learning for First-order Logic-based Abduction , 2012 .

[18]  Diego Mollá Aliod,et al.  Recognizing Textual Entailment Via Atomic Propositions , 2005, MLCW.

[19]  Jerry R. Hobbs,et al.  Coreference Resolution with ILP-based Weighted Abduction , 2012, COLING.

[20]  Stefan Thater,et al.  Assessing the impact of frame semantics on textual entailment , 2009, Natural Language Engineering.

[21]  Mitsuru Ishizuka,et al.  Fast Hypothetical Reasoning by Parallel Processing , 2000, PRICAI.

[22]  Jerry R. Hobbs,et al.  Implementing Weighted Abduction in Markov Logic , 2011, IWCS.

[23]  Johan Bos,et al.  Recognising Textual Entailment with Logical Inference , 2005, HLT.

[24]  Jerry R. Hobbs.  Ontological Promiscuity , 1985, ACL.

[25]  William J. Cook,et al.  Solution of a Large-Scale Traveling-Salesman Problem , 1954, 50 Years of Integer Programming.

[26]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[27]  Uwe Reyle,et al.  From Discourse to Logic - Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory , 1993, Studies in linguistics and philosophy.

[28]  Oren Etzioni,et al.  Machine Reading , 2006, AAAI.

[29]  Kentaro Inui,et al.  Large-Scale Cost-Based Abduction in Full-Fledged First-Order Predicate Logic with Cutting Plane Inference , 2012, JELIA.

[30]  David L. Davidson,et al.  The Logical Form of Action Sentences , 2001 .

[31]  Ekaterina Ovchinnikova,et al.  Integration of World Knowledge for Natural Language Understanding , 2012, Atlantis Thinking Machines.

[32]  Robert P. Goldman,et al.  A Probabilistic Model of Plan Recognition , 1991, AAAI.

[33]  Khalid Choukri,et al.  The european language resources association , 1998, LREC.

[34]  Andrew Hickl,et al.  A Discourse Commitment-Based Framework for Recognizing Textual Entailment , 2007, ACL-PASCAL@ACL.

[35]  Hwee Tou Ng,et al.  Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation , 1992, KR.

[36]  Eduard H. Hovy,et al.  Filling Knowledge Gaps in Text for Machine Reading , 2010, COLING.

[37]  Dan I. Moldovan,et al.  Applying COGEX to Recognize Textual Entailment , 2005, MLCW.

[38]  Patrick Pantel,et al.  VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations , 2004, EMNLP.

[39]  Ashraf M. Abdelbar,et al.  An efficient LP-based admissible heuristic for cost-based abduction , 2005, J. Exp. Theor. Artif. Intell..

[40]  Eugene Santos,et al.  Polynomial Solvability of Cost-Based Abduction , 1996, Artif. Intell..

[41]  Rohit J. Kate and Raymond J. Mooney.  Probabilistic Abduction using Markov Logic Networks , 2009 .

[42]  Johan Bos,et al.  Wide-Coverage Semantic Analysis with Boxer , 2008, STEP.

[43]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[44]  Sebastian Riedel.  Improving the Accuracy and Efficiency of MAP Inference for Markov Logic , 2008, UAI.

[45]  Dan I. Moldovan,et al.  A Semantic Approach to Recognizing Textual Entailment , 2005, HLT.

[46]  Robert P. Goldman,et al.  Plan Recognition in Stories and in Life , 2013, UAI.

[47]  Noah A. Smith,et al.  Proceedings of EMNLP , 2007 .

[48]  Jerry R. Hobbs,et al.  Learning from Reading Syntactically Complex Biology Texts , 2007, AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning.

[49]  Jerry R. Hobbs,et al.  Interpretation as Abduction , 1993, Artif. Intell..

[50]  Ido Dagan,et al.  Recognizing textual entailment: Rational, evaluation and approaches – Erratum , 2010, Natural Language Engineering.

[51]  Josef Ruppenhofer,et al.  FrameNet II: Extended theory and practice , 2006 .

[52]  Mirella Lapata,et al.  Using Semantic Roles to Improve Question Answering , 2007, EMNLP.

[53]  Christiane Fellbaum,et al.  On the Role of Lexical and World Knowledge in RTE3 , 2007, ACL-PASCAL@ACL.

[54]  Curry I. Guinn,et al.  The Parallelization of Membrane Computers to Find Near Optimal Solutions to Cost-Based Abduction , 2008, GEM.

[55]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.