Rui Fang


2022

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HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold
Ruihan Zhang | Wei Wei | Xian-Ling Mao | Rui Fang | Dangyang Chen
Findings of the Association for Computational Linguistics: EMNLP 2022

Event detection has been suffering from constantly emerging event types with lack of sufficient data. Existing works formulate the new problem as few-shot event detection (FSED), and employ two-stage or unified models based on meta-learning to address the problem. However, these methods fall far short of expectations due to: (i) insufficient learning of discriminative representations in low-resource scenarios, and (ii) representation overlap between triggers and non-triggers. To resolve the above issues, in this paper, we propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCL-TAT), which enables discriminative representation learning with a two-view contrastive loss (support-support and prototype-query), and devises an easily-adapted threshold to alleviate misidentification of triggers. Extensive experiments on the benchmark dataset FewEvent demonstrate the superiority of our method to achieve better results compared to the state-of-the-arts. All the data and codes will be available to facilitate future research.

2017

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funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
Quanzhi Li | Armineh Nourbakhsh | Xiaomo Liu | Rui Fang | Sameena Shah
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D & E for English), all of which are about topic-based message polarity classification. Our team is ranked #6 in subtask B, #3 by MAEu and #9 by MAEm in subtask C, #3 using RAE and #6 using KLD in subtask D, and #3 in subtask E.

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funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
Quanzhi Li | Sameena Shah | Armineh Nourbakhsh | Rui Fang | Xiaomo Liu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm

2016

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Witness Identification in Twitter
Rui Fang | Armineh Nourbakhsh | Xiaomo Liu | Sameena Shah | Quanzhi Li
Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media

2014

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Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse
Changsong Liu | Lanbo She | Rui Fang | Joyce Y. Chai
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Modeling Collaborative Referring for Situated Referential Grounding
Changsong Liu | Rui Fang | Lanbo She | Joyce Chai
Proceedings of the SIGDIAL 2013 Conference

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Towards Situated Dialogue: Revisiting Referring Expression Generation
Rui Fang | Changsong Liu | Lanbo She | Joyce Y. Chai
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Towards Mediating Shared Perceptual Basis in Situated Dialogue
Changsong Liu | Rui Fang | Joyce Chai
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue