Topic Identification in Discourse
Resource
Proceedings of the 7th Conference of the European Chapter of Association for Computational Linguistics, 267-271
Journal
Seventh conference on European chapter of the Association for Computational Linguistics
Pages
267
Date Issued
1995
Date
1995
Author(s)
Abstract
This paper proposes a corpus-based language model for topic identification. We analyze the association of noun-noun and noun-verb pairs in LOB Corpus. The word association norms are based on three factors: 1) word importance, 2) pair co-occurrence, and 3) distance. They are trained on the paragraph and sentence levels for noun-noun and noun-verb pairs, respectively. Under the topic coherence postulation, the nouns that have the strongest connectivities with the other nouns and verbs in the discourse form the preferred topic set. The collocational semantics then is used to identify the topics from paragraphs and to discuss the topic shift phenomenon among paragraphs.
SDGs
Publisher
Dublin, Ireland: EACL
Type
conference paper