Why-Question Answering using Intra- and Inter-Sentential Causal Relations

In this paper, we explore the utility of intra- and inter-sentential causal relations between terms or clauses as evidence for answering why-questions. To the best of our knowledge, this is the first work that uses both intra- and inter-sentential causal relations for why-QA. We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. (2012). By applying these ideas to Japanese why-QA, we improved precision by 4.4% against all the questions in our test set over the current state-of-theart system for Japanese why-QA. In addition, unlike the state-of-the-art system, our system could achieve very high precision (83.2%) for 25% of all the questions in the test set by restricting its output to the confident answers only.

[1]  Dan Roth,et al.  Minimally Supervised Event Causality Identification , 2011, EMNLP.

[2]  Jong-Hoon Oh,et al.  Relation Acquisition using Word Classes and Partial Patterns , 2011, EMNLP.

[3]  Mehwish Riaz,et al.  Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[4]  Jong-Hoon Oh,et al.  Why Question Answering using Sentiment Analysis and Word Classes , 2012, EMNLP.

[5]  Mihai Surdeanu,et al.  Learning to Rank Answers to Non-Factoid Questions from Web Collections , 2011, CL.

[6]  Ryuichiro Higashinaka,et al.  Corpus-based Question Answering for why-Questions , 2008, IJCNLP.

[7]  Kentaro Torisawa,et al.  Inducing Gazetteers for Named Entity Recognition by Large-Scale Clustering of Dependency Relations , 2008, ACL.

[8]  Takashi Inui,et al.  Investigating the Characteristics of Causal Relations in Japanese Text , 2005, FCA@ACL.

[9]  Dan I. Moldovan,et al.  Causal Relation Extraction , 2008, LREC.

[10]  SurdeanuMihai,et al.  Learning to rank answers to non-factoid questions from web collections , 2011 .

[11]  Kentaro Torisawa.  Acquiring Inference Rules with Temporal Constraints by Using Japanese Coordinated Sentences and Noun-Verb Co-occurrences , 2006, HLT-NAACL.

[12]  Peter Clark,et al.  The Seventh PASCAL Recognizing Textual Entailment Challenge , 2011, TAC.

[13]  Roberto Basili,et al.  Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification , 2007, ACL.

[14]  Shuming Shi,et al.  Overview of NTCIR-9 RITE: Recognizing Inference in TExt , 2011, NTCIR.

[15]  Syin Chan,et al.  Extracting Causal Knowledge from a Medical Database Using Graphical Patterns , 2000, ACL.

[16]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[17]  Jong-Hoon Oh,et al.  Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster , 2013, ACL.

[18]  Lou Boves,et al.  Using Syntactic Information for Improving Why-Question Answering , 2008, COLING.

[19]  Masaki Murata,et al.  Large Scale Relation Acquisition Using Class Dependent Patterns , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[20]  Ion Androutsopoulos,et al.  A Survey of Paraphrasing and Textual Entailment Methods , 2009, J. Artif. Intell. Res..

[21]  Roxana Gîrju,et al.  Automatic Detection of Causal Relations for Question Answering , 2003, ACL 2003.

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

[23]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[24]  Ido Dagan,et al.  Recognizing textual entailment: Rational, evaluation and approaches , 2009, Natural Language Engineering.

[25]  Jong-Hoon Oh,et al.  Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web , 2012, EMNLP.

[26]  Sanda M. Harabagiu,et al.  Question Answering Based on Semantic Structures , 2004, COLING.

[27]  Wessel Kraaij,et al.  Bringing Why-QA to Web Search , 2011, ECIR.

[28]  Kira Radinsky,et al.  Learning causality for news events prediction , 2012, WWW.

[29]  Masaki Murata,et al.  A System for Answering Non-Factoid Japanese Questions by Using Passage Retrieval Weighted Based on Type of Answer , 2007, NTCIR.

[30]  Du-Seong Chang,et al.  Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities , 2006, Inf. Process. Manag..