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Wenting Tu

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5 papers

1 author row

AAAI Conference 2019 Conference Paper

Exploring Human-Like Reading Strategy for Abstractive Text Summarization

Min Yang
Qiang Qu
Wenting Tu
Ying Shen
Zhou Zhao
Xiaojun Chen

The recent artificial intelligence studies have witnessed great interest in abstractive text summarization. Although remarkable progress has been made by deep neural network based methods, generating plausible and high-quality abstractive summaries remains a challenging task. The human-like reading strategy is rarely explored in abstractive text summarization, which however is able to improve the effectiveness of the summarization by considering the process of reading comprehension and logical thinking. Motivated by the humanlike reading strategy that follows a hierarchical routine, we propose a novel Hybrid learning model for Abstractive Text Summarization (HATS). The model consists of three major components, a knowledge-based attention network, a multitask encoder-decoder network, and a generative adversarial network, which are consistent with the different stages of the human-like reading strategy. To verify the effectiveness of HATS, we conduct extensive experiments on two real-life datasets, CNN/Daily Mail and Gigaword datasets. The experimental results demonstrate that HATS achieves impressive results on both datasets.

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AAAI Conference 2017 Short Paper

Attention Based LSTM for Target Dependent Sentiment Classification

Min Yang
Wenting Tu
Jingxuan Wang
Fei Xu
Xiaojun Chen

We present an attention-based bidirectional LSTM approach to improve the target-dependent sentiment classiﬁcation. Our method learns the alignment between the target entities and the most distinguishing features. We conduct extensive experiments on a real-life dataset. The experimental results show that our model achieves state-of-the-art results.

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AAAI Conference 2015 Conference Paper

Improving Microblog Retrieval from Exterior Corpus by Automatically Constructing Microblogging Corpus

Wenting Tu
David Cheung
Nikos Mamoulis

A large-scale training corpus consisting of microblogs belonging to a desired category is important for highaccuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and laborconsuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.

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AAAI Conference 2015 Conference Paper

Ordering-Sensitive and Semantic-Aware Topic Modeling

Min Yang
Tianyi Cui
Wenting Tu

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the “bag-of-words” assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.

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AAAI Conference 2015 Conference Paper

Time-Sensitive Opinion Mining for Prediction

Wenting Tu
David Cheung
Nikos Mamoulis

Users commonly use Web 2. 0 platforms to post their opinions and their predictions about future events (e. g. , the movement of a stock). Therefore, opinion mining can be used as a tool for predicting future events. Previous work on opinion mining extracts from the text only the polarity of opinions as sentiment indicators. We observe that a typical opinion post also contains temporal references which can improve prediction. This short paper presents our preliminary work on extracting reference time tags and integrating them into an opinion mining model, in order to improve the accuracy of future event prediction. We conduct an experimental evaluation using a collection of microblogs posted by investors to demonstrate the effectiveness of our approach.

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