Topic Model over Short Texts Incorporating Word Embedding

DOI

10.2991/aeecs-18.2018.34How to use a DOI?

Keywords

Short texts, Topic model, Word embedding, Text mining

Abstract

Short texts' data sparsity makes them difficult to find out their document-level word co-occurrence patterns, that's why conventional topic models like LDA experience a large performance degradation over short texts. As a derivative product of learning neuro probabilistic language model, word embedding can well express semantic similarity of word. In this paper, we propose a new model called promotion-BTM, which promotes the probability that similar words based on word embedding belong to the same topic. It also distinguishes the words of a biterm into topical word and general word, and only promotes topical words' semantically similar words. Extensive experiments on real-world datasets show that our model exceeds the baseline model BTM on all evaluations.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kai Yu
AU  - Yiming Zhang
AU  - Xu Wang
PY  - 2018/03
DA  - 2018/03
TI  - Topic Model over Short Texts Incorporating Word Embedding
BT  - Proceedings of the 2018 2nd International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2018)
PB  - Atlantis Press
SP  - 194
EP  - 200
SN  - 2352-5401
UR  - https://doi.org/10.2991/aeecs-18.2018.34
DO  - 10.2991/aeecs-18.2018.34
ID  - Yu2018/03
ER  -