@inproceedings{370bf4263b8347d48498b6d38fe1c50b,
title = "Text classification with enriched word features",
abstract = "Text classification is a fundamental task in natural language processing. Most existing text classification models focus on constructing sophisticated high-level text features but ignore the importance of word features. Those models only use low-level word features obtained from a linear layer as input. To explore how the quality of word representations affects text classification, we propose a deep architecture which can extract high-level word features to perform text classification. Specifically, we use different temporal convolution filters, which vary in size, to capture different contextual features. Then a transition layer is used to coalesce the contextual features and form an enriched high-level word representations. We also find that word feature reuse is useful in our architecture to enrich word representations. Extensive experiments on six publically available datasets show that enriched word representations can significantly improve the performance of classification models.",
keywords = "Enriched word representation, Temporal convolution, Text classification",
author = "Jingda Xu and Cheng Zhang and Peng Zhang and Dawei Song",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97310-4_31",
language = "English",
isbn = "9783319973098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "274--281",
editor = "Xin Geng and Byeong-Ho Kang",
booktitle = "PRICAI 2018",
address = "Germany",
}