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        <title>Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains</title>
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    <abstract>The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document’s words using lexical chains as a semantic representation of its meaning. Using a combination of diverse types of lexical chains, we develop a text document representation that can be used for semantic document retrieval. For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document. The extraction and processing of concepts is performed using WordNet as a lexical database. The segmentation then uses these lexical chains to model the dispersion of concepts in the document. Representing each document as a high-dimensional vector, we use spherical k-means clustering to demonstrate that our approach performs better than previous techniques.</abstract>
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%0 Conference Proceedings
%T Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains
%A Ruas, Terry
%A Grosky, William
%S Proceedings of the 9th Global Wordnet Conference
%D 2018
%8 January
%I Global Wordnet Association
%C Nanyang Technological University (NTU), Singapore
%F ruas-grosky-2018-semantic
%X The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document’s words using lexical chains as a semantic representation of its meaning. Using a combination of diverse types of lexical chains, we develop a text document representation that can be used for semantic document retrieval. For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document. The extraction and processing of concepts is performed using WordNet as a lexical database. The segmentation then uses these lexical chains to model the dispersion of concepts in the document. Representing each document as a high-dimensional vector, we use spherical k-means clustering to demonstrate that our approach performs better than previous techniques.
%U https://aclanthology.org/2018.gwc-1.11
%P 87-96

Markdown (Informal)

[Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains](https://aclanthology.org/2018.gwc-1.11) (Ruas & Grosky, GWC 2018)

• Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains (Ruas & Grosky, GWC 2018)

ACL

• Terry Ruas and William Grosky. 2018. Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains. In Proceedings of the 9th Global Wordnet Conference, pages 87–96, Nanyang Technological University (NTU), Singapore. Global Wordnet Association.