Inference Rules and their Application to Recognizing Textual Entailment

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 211–219, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Inference Rules and their Application to Recognizing Textual Entailment Georgiana Dinu Saarland University Campus, D-66123 Saarbr¨ucken dinu@coli.uni-sb.de Rui Wang Saarland University Campus, D-66123 Saarbr¨ucken rwang@coli.uni-sb.de Abstract In this paper, we explore ways of improv- ing an inference rule collection and its ap- plication to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and ob- tain more rules using a hand-crafted lex- ical resource. Following this, we derive a dependency-based structure representa- tion from texts, which aims to provide a proper base for the inference rule appli- cation. The evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible im- provements. 1 Introduction Textual inference plays an important role in many natural language processing (NLP) tasks. In recent years, the recognizing textual entailment (RTE) (Dagan et al., 2006) challenge, which focuses on detecting semantic inference, has attracted a lot of attention. Given a text T (several sentences) and a hypothesis H (one sentence), the goal is to detect if H can be inferred from T. Studies such as (Clark et al., 2007) attest that lexical substitution (e.g. synonyms, antonyms) or simple syntactic variation account for the entail- ment only in a small number of pairs. Thus, one essential issue is to identify more complex expres- sions which, in appropriate contexts, convey the same (or similar) meaning. However, more gener- ally, we are also interested in pairs of expressions in which only a uni-directional inference relation holds1. 1We will use the term inference rule to stand for such con- cept; the two expressions can be actual paraphrases if the re- lation is bi-directional A typical example is the following RTE pair in which accelerate to in H is used as an alternative formulation for reach speed of in T. T: The high-speed train, scheduled for a trial run on Tues- day, is able to reach a maximum speed of up to 430 kilome- ters per hour, or 119 meters per second. H: The train accelerates to 430 kilometers per hour. One way to deal with textual inference is through rule representation, for example X wrote Y ≈ X is author of Y. However, manually building collections of inference rules is time-consuming and it is unlikely that humans can exhaustively enumerate all the rules encoding the knowledge needed in reasoning with natural language. In- stead, an alternative is to acquire these rules au- tomatically from large corpora. Given such a rule collection, the next step to focus on is how to suc- cessfully use it in NLP applications. This paper tackles both aspects, acquiring inference rules and using them for the task of recognizing textual en- tailment. For the first aspect, we extend and refine an ex- isting collection of …

Content truncated to 500 words