Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification

Konstantinos Skianis; Fragkiskos Malliaros; Michalis Vazirgiannis

Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification

Konstantinos Skianis ¹ Fragkiskos Malliaros ^{2, 3} Michalis Vazirgiannis ¹

1 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]

2 CVN - Centre de vision numérique

3 GALEN-POST - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content

Inria Saclay - Ile de France

Abstract : Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words (GoW) model in which each document is represented by a graph that encodes relationships between the different terms. Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce novel graph-based weighting schemes by enriching graphs with word-embedding similarities, in order to reward or penalize semantic relationships. Our methods produce more discriminative feature weights for text categorization, out-performing existing frequency-based criteria.

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

Computer Science [cs] / Artificial Intelligence [cs.AI]

Computer Science [cs] / Document and Text Processing

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https://hal-centralesupelec.archives-ouvertes.fr/hal-01848880
Contributor : Fragkiskos Malliaros <>
Submitted on : Wednesday, July 25, 2018 - 12:02:11 PM
Last modification on : Wednesday, March 27, 2019 - 4:41:28 PM
Long-term archiving on : Friday, October 26, 2018 - 1:32:55 PM

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