Comparative study of word embedding methods in topic segmentation | Zendy

Marwa Naili | Zendy; Anja Habacha Chaïbi | Zendy; Henda Hajjami Ben Ghézala | Zendy

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Comparative study of word embedding methods in topic segmentation

Author(s) -

Marwa Naili,

Anja Habacha Chaïbi,

Henda Hajjami Ben Ghézala

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.009

Subject(s) - word2vec , computer science , natural language processing , word embedding , artificial intelligence , word (group theory) , context (archaeology) , text segmentation , representation (politics) , field (mathematics) , embedding , segmentation , linguistics , paleontology , philosophy , mathematics , politics , political science , pure mathematics , law , biology

The vector representations of words are very useful in different natural language processing tasks in order to capture the semantic meaning of words. In this context, the three known methods are: LSA, Word2Vec and GloVe. In this paper, these methods will be investigated in the field of topic segmentation for both languages Arabic and English. Moreover, Word2Vec is studied in depth by using different models and approximation algorithms. As results, we found out that LSA, Word2Vec and GloVe depend on the used language. However, Word2Vec presents the best word vector representation yet it depends on the choice of model.

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