Qing Gu


2020

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A Symmetric Local Search Network for Emotion-Cause Pair Extraction
Zifeng Cheng | Zhiwei Jiang | Yafeng Yin | Hua Yu | Qing Gu
Proceedings of the 28th International Conference on Computational Linguistics

Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses individually, then paired the emotion and cause clauses, and filtered out the pairs without causality. Different from this method that separated the detection and the matching of emotion and cause into two steps, we propose a Symmetric Local Search Network (SLSN) model to perform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the emotion subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs. Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods.

2018

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Enriching Word Embeddings with Domain Knowledge for Readability Assessment
Zhiwei Jiang | Qing Gu | Yafeng Yin | Daoxu Chen
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we present a method which learns the word embedding for readability assessment. For the existing word embedding models, they typically focus on the syntactic or semantic relations of words, while ignoring the reading difficulty, thus they may not be suitable for readability assessment. Hence, we provide the knowledge-enriched word embedding (KEWE), which encodes the knowledge on reading difficulty into the representation of words. Specifically, we extract the knowledge on word-level difficulty from three perspectives to construct a knowledge graph, and develop two word embedding models to incorporate the difficulty context derived from the knowledge graph to define the loss functions. Experiments are designed to apply KEWE for readability assessment on both English and Chinese datasets, and the results demonstrate both effectiveness and potential of KEWE.

2015

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A Graph-based Readability Assessment Method using Word Coupling
Zhiwei Jiang | Gang Sun | Qing Gu | Tao Bai | Daoxu Chen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing