Papers › Task-oriented Word Embedding for Text Classification

Task-oriented Word Embedding for Text Classification

1 Aug 2018COLING 2018 8archive 2025-07-28

Qian Liu, He-Yan Huang, Yang Gao, Xiaochi Wei, Yuxin Tian, Luyang Liu

Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.

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