Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities

dc.creatorRadev, Dragomir R.
dc.date1998-05-31
dc.date.accessioned2026-07-07T02:36:14Z
dc.date.available2026-07-07T02:36:14Z
dc.descriptionThis paper presents the results of a study on the semantic constraints imposed on lexical choice by certain contextual indicators. We show how such indicators are computed and how correlations between them and the choice of a noun phrase description of a named entity can be automatically established using supervised learning. Based on this correlation, we have developed a technique for automatic lexical choice of descriptions of entities in text generation. We discuss the underlying relationship between the pragmatics of choosing an appropriate description that serves a specific purpose in the automatically generated text and the semantics of the description itself. We present our work in the framework of the more general concept of reuse of linguistic structures that are automatically extracted from large corpora. We present a formal evaluation of our approach and we conclude with some thoughts on potential applications of our method.
dc.description7 pages, uses colacl.sty and acl.bst, uses epsfig. To appear in the Proceedings of the Joint 17th International Conference on Computational Linguistics 36th Annual Meeting of the Association for Computational Linguistics (COLING-ACL'98)
dc.identifierhttps://arxiv.org/abs/cmp-lg/9806001
dc.identifierhttp://arxiv.org/abs/cmp-lg/9806001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/15845
dc.subjectComputation and Language
dc.titleLearning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities
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