PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Natural Language as the Basis for Meaning Representation and Inference
Ido Dagan, Roy Bar Haim, Idan Szpektor, Iddo Greental and Eyal Shnarch
In: Computational Linguistics and Intelligent Text Processing Lecture Notes in Computer Science , 4919 . (2008) Springer , pp. 151-170.


Semantic inference is an important component in many natural language understanding applications. Classical approaches to semantic inference rely on logical representations for meaning, which may be viewed as being “external” to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe a generic semantic inference framework that operates directly on language-based structures, particularly syntactic trees. New trees are inferred by applying entailment rules, which provide a unified representation for varying types of inferences. Rules were generated by manual and automatic methods, covering generic linguistic structures as well as specific lexical-based inferences. Initial empirical evaluation in a Relation Extraction setting supports the validity and potential of our approach. Additionally, such inference is shown to improve the critical step of unsupervised learning of entailment rules, which in turn enhances the scope of the inference system. This paper corresponds to the invited talk of the first author at CICLING 2008.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:3852
Deposited By:Ido Dagan
Deposited On:25 February 2008