Extraction of salient textual patterns: Synergy between lexical cohesion and contextual coherence
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AbstractMost current information retrieval systems rely solely on lexical item repetition, which is notorious for its vulnerability. In this research, we propose a novel method for the extraction of salient textual patterns. One of our major objectives is to move away from keywords and their associated limitations in textual information retrieval. How individual sentences in text fit together to be perceived as a salient pattern is identified. A text network that exhibits textual continuity, arising from a connectionist model, is described. The network facilitates a dynamic extraction of salient textual segments by capturing semantics from two different categories of natural language, namely lexical cohesion and contextual coherence. We also present the results of an empirical study designed to compare our model with the performance of human judges in the identification of salient textual patterns. The preliminary results show that our model has the potential for automatic salient patterns discovery in text.
All Author(s) ListChan SWK
Journal nameIEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans
Volume Number34
Issue Number2
Pages205 - 218
LanguagesEnglish-United Kingdom
Keywordsconnectionist model; knowledge extraction; salient textual pattern discovery; semantic relatedness
Web of Science Subject CategoriesComputer Science; Computer Science, Cybernetics; COMPUTER SCIENCE, CYBERNETICS; Computer Science, Theory & Methods; COMPUTER SCIENCE, THEORY & METHODS

Last updated on 2020-01-07 at 04:13