Improving Word Embeddings with Convolutional Feature Learning and Subword Information

AAAI 2017 pp. 3144-3151

doi:10.1609/AAAI.V31I1.10993 /aaai/2017/cao2017aaai-improving/

Abstract

We present a novel approach to learning word embeddings by exploring subword information (character n-gram, root/affix and inflections) and capturing the structural information of their context with convolutional feature learning. Specifically, we introduce a convolutional neural network architecture that allows us to measure structural information of context words and incorporate subword features conveying semantic, syntactic and morphological information related to the words. To assess the effectiveness of our model, we conduct extensive experiments on the standard word similarity and word analogy tasks. We showed improvements over existing state-of-the-art methods for learning word embeddings, including skipgram, GloVe, char n-gram and DSSM.

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Text

Cao and Lu. "Improving Word Embeddings with Convolutional Feature Learning and Subword Information." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10993

Markdown

[Cao and Lu. "Improving Word Embeddings with Convolutional Feature Learning and Subword Information." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/cao2017aaai-improving/) doi:10.1609/AAAI.V31I1.10993

BibTeX

@inproceedings{cao2017aaai-improving,
  title     = {{Improving Word Embeddings with Convolutional Feature Learning and Subword Information}},
  author    = {Cao, Shaosheng and Lu, Wei},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {3144-3151},
  doi       = {10.1609/AAAI.V31I1.10993},
  url       = {https://mlanthology.org/aaai/2017/cao2017aaai-improving/}
}