Fang Li


2018

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Encoding Sentiment Information into Word Vectors for Sentiment Analysis
Zhe Ye | Fang Li | Timothy Baldwin
Proceedings of the 27th International Conference on Computational Linguistics

General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks. The goal of this paper is to encode sentiment knowledge into pre-trained word vectors to improve the performance of sentiment analysis. Our proposed method is based on a convolutional neural network (CNN) and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method improves the accuracy of sentiment analysis compared to a number of benchmark methods.

2017

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Group Linguistic Bias Aware Neural Response Generation
Jianan Wang | Xin Wang | Fang Li | Zhen Xu | Zhuoran Wang | Baoxun Wang
Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing

For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.

2010

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SJTULTLAB: Chunk Based Method for Keyphrase Extraction
Letian Wang | Fang Li
Proceedings of the 5th International Workshop on Semantic Evaluation