Hua Xu


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CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality
Wenmeng Yu | Hua Xu | Fanyang Meng | Yilin Zhu | Yixiao Ma | Jiele Wu | Jiyun Zou | Kaicheng Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at


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Deep Unknown Intent Detection with Margin Loss
Ting-En Lin | Hua Xu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.

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The Strength of the Weakest Supervision: Topic Classification Using Class Labels
Jiatong Li | Kai Zheng | Hua Xu | Qiaozhu Mei | Yue Wang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a ”free” warm start to the topic classifier.


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UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes
Hee-Jin Lee | Hua Xu | Jingqi Wang | Yaoyun Zhang | Sungrim Moon | Jun Xu | Yonghui Wu
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Clinical Abbreviation Disambiguation Using Neural Word Embeddings
Yonghui Wu | Jun Xu | Yaoyun Zhang | Hua Xu
Proceedings of BioNLP 15

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UTH-CCB: The Participation of the SemEval 2015 Challenge – Task 14
Jun Xu | Yaoyun Zhang | Jingqi Wang | Yonghui Wu | Min Jiang | Ergin Soysal | Hua Xu
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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UTH_CCB: A report for SemEval 2014 – Task 7 Analysis of Clinical Text
Yaoyun Zhang | Jingqi Wang | Buzhou Tang | Yonghui Wu | Min Jiang | Yukun Chen | Hua Xu
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)


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Implicit Feature Detection via a Constrained Topic Model and SVM
Wei Wang | Hua Xu | Xiaoqiu Huang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


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Soochow University: Description and Analysis of the Chinese Word Sense Induction System for CLP2010
Hua Xu | Bing Liu | Longhua Qian | Guodong Zhou
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints
Zhongwu Zhai | Bing Liu | Hua Xu | Peifa Jia
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine
Son Doan | Hua Xu
Coling 2010: Posters


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Combining multiple evidence for gene symbol disambiguation
Hua Xu | Jung-Wei Fan | Carol Friedman
Biological, translational, and clinical language processing