Liang Yang


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基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)
Jinhui Zhang (张瑾晖) | Shaowu Zhang (张绍武) | Xiaochao Fan (樊小超) | Liang Yang (杨亮) | Hongfei Lin (林鸿飞)
Proceedings of the 19th Chinese National Conference on Computational Linguistics


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ALBERT-BiLSTM for Sequential Metaphor Detection
Shuqun Li | Jingjie Zeng | Jinhui Zhang | Tao Peng | Liang Yang | Hongfei Lin
Proceedings of the Second Workshop on Figurative Language Processing

In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.


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Transformer-Based Capsule Network For Stock Movement Prediction
Jintao Liu | Hongfei Lin | Xikai Liu | Bo Xu | Yuqi Ren | Yufeng Diao | Liang Yang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing


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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
Yufeng Diao | Hongfei Lin | Di Wu | Liang Yang | Kan Xu | Zhihao Yang | Jian Wang | Shaowu Zhang | Bo Xu | Dongyu Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.

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Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
Dongyu Zhang | Hongfei Lin | Liang Yang | Shaowu Zhang | Bo Xu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Metaphors are frequently used to convey emotions. However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions. Furthermore, most studies focus on English, and few in other languages, particularly Sino-Tibetan languages such as Chinese, for emotion analysis from metaphorical texts, although there are likely to be many differences in emotional expressions of metaphorical usages across different languages. We therefore construct a significant new corpus on metaphor, with 5,605 manually annotated sentences in Chinese. We present an annotation scheme that contains annotations of linguistic metaphors, emotional categories (joy, anger, sadness, fear, love, disgust and surprise), and intensity. The annotation agreement analyses for multiple annotators are described. We also use the corpus to explore and analyze the emotionality of metaphors. To the best of our knowledge, this is the first relatively large metaphor corpus with an annotation of emotions in Chinese.