Peng Jin


2020

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CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
Yice Zhang | Jiaxuan Lin | Yang Fan | Peng Jin | Yuanchao Liu | Bingquan Liu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.

2014

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Multi-view Chinese Treebanking
Likun Qiu | Yue Zhang | Peng Jin | Houfeng Wang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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CLTC: A Chinese-English Cross-lingual Topic Corpus
Yunqing Xia | Guoyu Tang | Peng Jin | Xia Yang
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Cross-lingual topic detection within text is a feasible solution to resolving the language barrier in accessing the information. This paper presents a Chinese-English cross-lingual topic corpus (CLTC), in which 90,000 Chinese articles and 90,000 English articles are organized within 150 topics. Compared with TDT corpora, CLTC has three advantages. First, CLTC is bigger in size. This makes it possible to evaluate the large-scale cross-lingual text clustering methods. Second, articles are evenly distributed within the topics. Thus it can be used to produce test datasets for different purposes. Third, CLTC can be used as a cross-lingual comparable corpus to develop methods for cross-lingual information access. A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods.

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SemEval-2012 Task 4: Evaluating Chinese Word Similarity
Peng Jin | Yunfang Wu
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2010

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SemEval-2010 Task 18: Disambiguating Sentiment Ambiguous Adjectives
Yunfang Wu | Peng Jin
Proceedings of the 5th International Workshop on Semantic Evaluation

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SemEval-2 Task 15: Infrequent Sense Identification for Mandarin Text to Speech Systems
Peng Jin | Yunfang Wu
Proceedings of the 5th International Workshop on Semantic Evaluation

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The Chinese Persons Name Diambiguation Evaluation: Exploration of Personal Name Disambiguation in Chinese News
Ying Chen | Peng Jin | Wenjie Li | Chu-Ren Huang
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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LSTC System for Chinese Word Sense Induction
Peng Jin | Yihao Zhang | Rui Sun
CIPS-SIGHAN Joint Conference on Chinese Language Processing

2009

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Estimating and Exploiting the Entropy of Sense Distributions
Peng Jin | Diana McCarthy | Rob Koeling | John Carroll
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2007

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SemEval-2007 Task 05: Multilingual Chinese-English Lexical Sample
Peng Jin | Yunfang Wu | Shiwen Yu
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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PKU: Combining Supervised Classifiers with Features Selection
Peng Jin | Danqing Zhu | Fuxin Li | Yunfang Wu
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Building Chinese Sense Annotated Corpus with the Help of Software Tools
Yunfang Wu | Peng Jin | Tao Guo | Shiwen Yu
Proceedings of the Linguistic Annotation Workshop