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Yunqing Xia

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4 papers

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IS Journal 2020 Journal Article

Commonsense Knowledge Enhanced Memory Network for Stance Classification

Jiachen Du
Lin Gui
Ruifeng Xu
Yunqing Xia
Xuan Wang

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.

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IJCAI Conference 2016 Conference Paper

Bag-of-Embeddings for Text Classification

Peng Jin
Yue Zhang
Xingyuan Chen
Yunqing Xia

Words are central to text classification. It has been shown that simple Naive Bayes models with word and bigram features can give highly competitive accuracies when compared to more sophisticated models with part-of-speech, syntax and semantic features. Embeddings offer distributional features about words. We study a conceptually simple classification model by exploiting multi-prototype word embeddings based on text classes. The key assumption is that words exhibit different distributional characteristics under different text classes. Based on this assumption, we train multi-prototype distributional word representations for different text classes. Given a new document, its text class is predicted by maximizing the probabilities of embedding vectors of its words under the class. In two standard classification benchmark datasets, one is balance and the other is imbalance, our model outperforms state-of-the-art systems, on both accuracy and macro-average F-1 score.

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AAAI Conference 2015 Conference Paper

Dataless Text Classification with Descriptive LDA

Xingyuan Chen
Yunqing Xia
Peng Jin
John Carroll

Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios.

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IS Journal 2013 Journal Article

New Avenues in Opinion Mining and Sentiment Analysis

Erik Cambria
Bjorn Schuller
Yunqing Xia
Catherine Havasi

The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.

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