Unsupervised Learning of Semantic Relation Composition

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1456–1465, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Unsupervised Learning of Semantic Relation Composition Eduardo Blanco and Dan Moldovan Human Language Technology Research Institute The University of Texas at Dallas Richardson, TX 75080 USA {eduardo,moldovan}@hlt.utdallas.edu Abstract This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is indepen- dent of any particular relation inventory. It relies on describing semantic relations using primitives and manipulating these primitives according to an algebra. The method was tested using a set of eight semantic relations yielding 78 inference axioms which were eval- uated over PropBank. 1 Introduction Capturing the meaning of text is a long term goal within the NLP community. Whereas during the last decade the field has seen syntactic parsers mature and achieve high performance, the progress in se- mantics has been more modest. Previous research has mostly focused on relations between particular kind of arguments, e.g., semantic roles, noun com- pounds. Notwithstanding their significance, they target a fairly narrow text semantics compared to the broad semantics encoded in text. Consider the sentence in Figure 1. Semantic role labelers exclusively detect the relations indicated with solid arrows, which correspond to the sentence syntactic dependencies. On top of those roles, there are at least three more relations (discontinuous ar- rows) that encode semantics other than the verb- argument relations. In this paper, we venture beyond semantic rela- tion extraction from text and investigate techniques to compose them. We explore the idea of inferring S NP VP A man . . . AGT V PP NP VP came AGT before the . . . LOC LOC yesterday TMP TMP to talk . . . PRP Figure 1: Semantic representation of A man from the Bush administration came before the House Agricultural Committee yesterday to talk about ... (wsj 0134, 0). a new relation linking the ends of a chain of rela- tions. This scheme, informally used previously for combining HYPERNYM with other relations, has not been studied for arbitrary pairs of relations. For example, it seems adequate to state the fol- lowing: if x is PART-OF y and y is HYPERNYM of z, then x is PART-OF z. An inference using this rule can be obtained instantiating x, y and z with engine, car and convertible. Going a step further, we consider nonobvious inferences involving AGENT, PURPOSE and other semantic relations. The novelties of this paper are twofold. First, an extended definition for semantic relations is pro- posed, including (1) semantic restrictions for their domains and ranges, and (2) semantic primitives. Second, an algorithm for obtaining inference ax- ioms is described. Axioms take as their premises chains of two relations and output a new relation linking the ends of the chain. This adds an extra layer of semantics on top of previously extracted re- 1456 Primitive Description Inv. Ref. 1: Composable Relation can be meaningfully composed with other relations due to their …

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