Ming Wang


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A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation
Ming Wang | Yinglin Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks. In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses. Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-of-the-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure. When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20% SemCor data.


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YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Ming Wang | Biao Chu | Qingxun Liu | Xiaobing Zhou
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five-tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B and C, respectively.