@inproceedings{ruas-grosky-2018-semantic,
    title = "Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains",
    author = "Ruas, Terry  and
      Grosky, William",
    editor = "Bond, Francis  and
      Vossen, Piek  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2018.gwc-1.11/",
    pages = "87--96",
    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."
}