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        <title>Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification</title>
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        <namePart type="given">Konstantinos</namePart>
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    <name type="personal">
        <namePart type="given">Michalis</namePart>
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            <namePart type="family">Glavaš</namePart>
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            <namePart type="given">Swapna</namePart>
            <namePart type="family">Somasundaran</namePart>
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    <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, outperforming existing frequency-based criteria.</abstract>
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%0 Conference Proceedings
%T Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification
%A Skianis, Konstantinos
%A Malliaros, Fragkiskos
%A Vazirgiannis, Michalis
%Y Glavaš, Goran
%Y Somasundaran, Swapna
%Y Riedl, Martin
%Y Hovy, Eduard
%S Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F skianis-etal-2018-fusing
%X 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, outperforming existing frequency-based criteria.
%R 10.18653/v1/W18-1707
%U https://aclanthology.org/W18-1707/
%U https://doi.org/10.18653/v1/W18-1707
%P 49-58

Markdown (Informal)

[Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification](https://aclanthology.org/W18-1707/) (Skianis et al., TextGraphs 2018)

• Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification (Skianis et al., TextGraphs 2018)

ACL

• Konstantinos Skianis, Fragkiskos Malliaros, and Michalis Vazirgiannis. 2018. Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification. In Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12), pages 49–58, New Orleans, Louisiana, USA. Association for Computational Linguistics.