Mingyu Wan


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Affection Driven Neural Networks for Sentiment Analysis
Rong Xiang | Yunfei Long | Mingyu Wan | Jinghang Gu | Qin Lu | Chu-Ren Huang
Proceedings of the 12th Language Resources and Evaluation Conference

Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.

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Modality Enriched Neural Network for Metaphor Detection
Mingyu Wan | Baixi Xing
Proceedings of the 28th International Conference on Computational Linguistics

Metaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in ‘sweet voice’. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset–the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.

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Using Conceptual Norms for Metaphor Detection
Mingyu Wan | Kathleen Ahrens | Emmanuele Chersoni | Menghan Jiang | Qi Su | Rong Xiang | Chu-Ren Huang
Proceedings of the Second Workshop on Figurative Language Processing

This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and AllPOS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5% higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.


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PolyU_CBS-CFA at the FinSBD Task: Sentence Boundary Detection of Financial Data with Domain Knowledge Enhancement and Bilingual Training
Mingyu Wan | Rong Xiang | Emmanuele Chersoni | Natalia Klyueva | Kathleen Ahrens | Bin Miao | David Broadstock | Jian Kang | Amos Yung | Chu-Ren Huang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing


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A Re-examination of Syntactic Complexity by Investigating the Internal Structure Variations of Adverbial Clauses across Speech and Writing
Mingyu Wan | Alex Chengyu Fang
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation