Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo khoa học: "Unsupervised Learning of Semantic Relation Composition"

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is independent 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 evaluated over PropBank. | 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 independent 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 evaluated 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 semantics has been more modest. Previous research has mostly focused on relations between particular kind of arguments e.g. semantic roles noun compounds. 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 arrows that encode semantics other than the verbargument relations. In this paper we venture beyond semantic relation extraction from text and investigate techniques to compose them. We explore the idea of inferring 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 relations. 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