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GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs

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Abstract

Graphs have been widely used as modeling tools in Natural Language Processing (NLP), Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words representation is applied; that way, a document is represented as a multiset of its terms, disregarding dependencies between the terms. Although several variants and extensions of this modeling approach have been proposed, the main weakness comes from the underlying term independence assumption; the order of the terms within a document is completely disregarded and any relationship between terms is not taken into account in the final task. To deal with this problem, the research community has explored various representations, and to this direction, graphs constitute a well-developed model for text representation. The goal of this tutorial is to offer a comprehensive presentation of recent methods that rely on graph-based text representations to deal with various tasks in Text Mining, NLP and IR.
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Dates and versions

hal-01958927 , version 1 (18-12-2018)

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Michalis Vazirgiannis, Fragkiskos Malliaros, Giannis Nikolentzos. GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs. 27th ACM International Conference on Information and Knowledge Management (CIKM), Oct 2018, Torino, Italy. pp.2295-2296, ⟨10.1145/3269206.3274273⟩. ⟨hal-01958927⟩
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