Wenliang Chen


2020

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Improving Relation Extraction with Relational Paraphrase Sentences
Junjie Yu | Tong Zhu | Wenliang Chen | Wei Zhang | Min Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.

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Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
Tong Zhu | Haitao Wang | Junjie Yu | Xiabing Zhou | Wenliang Chen | Wei Zhang | Min Zhang
Proceedings of the 28th International Conference on Computational Linguistics

In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-scale annotated data by aligning entity pairs from Knowledge Bases (KB) to sentences. However, these DS-generated datasets inevitably have wrong labels that result in incorrect evaluation scores during testing, which may mislead the researchers. To solve this problem, we build a new dataset NYTH, where we use the DS-generated data as training data and hire annotators to label test data. Compared with the previous datasets, NYT-H has a much larger test set and then we can perform more accurate and consistent evaluation. Finally, we present the experimental results of several widely used systems on NYT-H. The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different. This indicates that our human-annotated data is necessary for evaluation of distantly-supervised relation extraction.

2018

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M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains
Qi Lu | YaoSheng Yang | Zhenghua Li | Wenliang Chen | Min Zhang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
Yaosheng Yang | Wenliang Chen | Zhenghua Li | Zhengqiu He | Min Zhang
Proceedings of the 27th International Conference on Computational Linguistics

A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets.

2016

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Active Learning for Dependency Parsing with Partial Annotation
Zhenghua Li | Min Zhang | Yue Zhang | Zhanyi Liu | Wenliang Chen | Hua Wu | Haifeng Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Distributed Representations for Building Profiles of Users and Items from Text Reviews
Wenliang Chen | Zhenjie Zhang | Zhenghua Li | Min Zhang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation systems with useful features on users and items. Our approach attempts to construct vectors to represent profiles of users and items under a unified framework to maximize word appearance likelihood. Then, the vector representations are used for a recommendation task in which we predict scores on unobserved user-item pairs without given texts. The recommendation-aware distributed representation approach is fully supported by effective and efficient learning algorithms over massive text archive. Our empirical evaluations on real datasets show that our system outperforms the state-of-the-art baseline systems.

2015

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Coupled Sequence Labeling on Heterogeneous Annotations: POS Tagging as a Case Study
Zhenghua Li | Jiayuan Chao | Min Zhang | Wenliang Chen
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Soft Cross-lingual Syntax Projection for Dependency Parsing
Zhenghua Li | Min Zhang | Wenliang Chen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Feature Embedding for Dependency Parsing
Wenliang Chen | Yue Zhang | Min Zhang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Dependency Parsing: Past, Present, and Future
Wenliang Chen | Zhenghua Li | Min Zhang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Tutorial Abstracts

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Ambiguity-aware Ensemble Training for Semi-supervised Dependency Parsing
Zhenghua Li | Min Zhang | Wenliang Chen
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Semi-Supervised Feature Transformation for Dependency Parsing
Wenliang Chen | Min Zhang | Yue Zhang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Fast and Accurate Shift-Reduce Constituent Parsing
Muhua Zhu | Yue Zhang | Wenliang Chen | Min Zhang | Jingbo Zhu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Utilizing Dependency Language Models for Graph-based Dependency Parsing Models
Wenliang Chen | Min Zhang | Haizhou Li
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data
Yiou Wang | Jun’ichi Kazama | Yoshimasa Tsuruoka | Wenliang Chen | Yujie Zhang | Kentaro Torisawa
Proceedings of 5th International Joint Conference on Natural Language Processing

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SMT Helps Bitext Dependency Parsing
Wenliang Chen | Jun’ichi Kazama | Min Zhang | Yoshimasa Tsuruoka | Yujie Zhang | Yiou Wang | Kentaro Torisawa | Haizhou Li
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Joint Models for Chinese POS Tagging and Dependency Parsing
Zhenghua Li | Min Zhang | Wanxiang Che | Ting Liu | Wenliang Chen | Haizhou Li
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Improving Graph-based Dependency Parsing with Decision History
Wenliang Chen | Jun’ichi Kazama | Yoshimasa Tsuruoka | Kentaro Torisawa
Coling 2010: Posters

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Bitext Dependency Parsing with Bilingual Subtree Constraints
Wenliang Chen | Jun’ichi Kazama | Kentaro Torisawa
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Multilingual Dependency Learning: A Huge Feature Engineering Method to Semantic Dependency Parsing
Hai Zhao | Wenliang Chen | Chunyu Kit | Guodong Zhou
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

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Multilingual Dependency Learning: Exploiting Rich Features for Tagging Syntactic and Semantic Dependencies
Hai Zhao | Wenliang Chen | Jun’ichi Kazama | Kiyotaka Uchimoto | Kentaro Torisawa
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

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Semantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection
Hai Zhao | Wenliang Chen | Chunyu Kit
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Improving Dependency Parsing with Subtrees from Auto-Parsed Data
Wenliang Chen | Jun’ichi Kazama | Kiyotaka Uchimoto | Kentaro Torisawa
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Learning Reliable Information for Dependency Parsing Adaptation
Wenliang Chen | Youzheng Wu | Hitoshi Isahara
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Dependency Parsing with Short Dependency Relations in Unlabeled Data
Wenliang Chen | Daisuke Kawahara | Kiyotaka Uchimoto | Yujie Zhang | Hitoshi Isahara
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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A Two-Stage Parser for Multilingual Dependency Parsing
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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An Empirical Study of Chinese Chunking
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Chinese Named Entity Recognition with Conditional Random Fields
Wenliang Chen | Yujie Zhang | Hitoshi Isahara
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

2005

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Using Multiple Discriminant Analysis Approach for Linear Text Segmentation
Jingbo Zhu | Na Ye | Xinzhi Chang | Wenliang Chen | Benjamin K Tsou
Second International Joint Conference on Natural Language Processing: Full Papers

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Some Studies on Chinese Domain Knowledge Dictionary and Its Application to Text Classification
Jingbo Zhu | Wenliang Chen
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing