NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases

2017-12-01 · IJCNLP 2017 12 · Yen-Hsuan Lee, Han-Yun Yeh, Yih-Ru Wang, Yuan-Fu Liao

In this paper, a deep phrase embedding approach using bi-directional long short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a local order-aware word, a global phrase embedding representations and a deep regression neural network (DRNN) model. The performance of the proposed method was benchmarked by the IJCNLP 2017 shared task 2. According the official evaluation results, our best system achieved mean rank 6.5 among all 24 submissions.

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