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        <title>CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases</title>
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    <name type="personal">
        <namePart type="given">Peng-Hsuan</namePart>
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        <namePart type="given">Hsin-Yang</namePart>
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    <abstract>CKIP takes part in solving the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP 2017. This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real values between 1 and 9. To achieve this, functions mapping Chinese character sequences to real numbers are built by regression techniques. In addition, the CKIP phrase Valence-Arousal (VA) predictor depends on knowledge of modifier words and head words. This includes the types of known modifier words, VA of head words, and distributional semantics of both these words. The predictor took the second place out of 13 teams on phrase VA prediction, with 0.444 MAE and 0.935 PCC on valence, and 0.395 MAE and 0.904 PCC on arousal.</abstract>
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%0 Conference Proceedings
%T CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases
%A Li, Peng-Hsuan
%A Ma, Wei-Yun
%A Wang, Hsin-Yang
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F li-etal-2017-ckip
%X CKIP takes part in solving the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP 2017. This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real values between 1 and 9. To achieve this, functions mapping Chinese character sequences to real numbers are built by regression techniques. In addition, the CKIP phrase Valence-Arousal (VA) predictor depends on knowledge of modifier words and head words. This includes the types of known modifier words, VA of head words, and distributional semantics of both these words. The predictor took the second place out of 13 teams on phrase VA prediction, with 0.444 MAE and 0.935 PCC on valence, and 0.395 MAE and 0.904 PCC on arousal.
%U https://aclanthology.org/I17-4014
%P 89-94

Markdown (Informal)

[CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases](https://aclanthology.org/I17-4014) (Li et al., IJCNLP 2017)

• CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases (Li et al., IJCNLP 2017)

ACL

• Peng-Hsuan Li, Wei-Yun Ma, and Hsin-Yang Wang. 2017. CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 89–94, Taipei, Taiwan. Asian Federation of Natural Language Processing.