Yu Hong

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Email: tianxianer@gmail.com

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2013

43.
Yan Weirong, Hong Yu, Zhu Shanshan, Yao Jianmin, Zhu Qiaoming.Implicit Discourse Relation Inference Based on Frame Semantics. Chinese Journal of Computers, 2013, Accepted.
42.
洪宇, 车婷婷, 严为绒, 梁颖红, 姚建民, 朱巧明, 周国栋.基于平行推理机制的隐式篇章关系检测方法研究. 软件学报. 2013. 已录用
41.
Yang Xuerong, Hong Yu, Ma Bin, Yao Jianmin, Zhu Qiaoming.Using Event Term and Entity Inference to Recognize Event Relation. Journal of Chinese Information, 2013, Accepted.
40.
Yang Xuerong, Hong Yu, Chen Yadong, Hui Fang, Yao Jianmin,Zhu Qiaoming.An Overview of Event Relation Detection. Journal of Chinese Information, 2013, Accepted.
39.
Ma Bin,Hong Yu,Yang Xuerong, Yao Jianmin, Zhu Qiaoming.Using Cross-Entity Inference to Improve Event Extraction. Journal of Chinese Information, 2013, Accepted.
38.
Che Tingting,Hong Yu,Zhou Xiaopei,Yan Weirong,Yao Jianmin, Zhu Qiaoming. Predicting Implicit Discourse Relation Based on Functional Connective.Journal of Chinese Information. 2013, Accepted.
37.
Ma Bin, Hong Yu, Yang Xuerong, Yao Jianmin, Zhu Qiaoming.Using Inference Cues to Recognize Event Relation. Acta Scientiarum Naturalium Universitatis Pekinensis (ASNUP), 2013, Accepted.
36.
Zhou Xiaopei,Hong Yu, Che Tingting, Yao Jianmin,Zhu Qiaoming. An Unsupervised Approach to Inferring Implicit Discourse Relation. ,Journal of Chinese Information, 2013, 27(2),p17-25.
35.
Ma Bin, Hong Yu, Zhang Jianfeng, Yao Jianmin,Zhu Qiaoming. A Thread-based Two-stage Clustering Method of Microblog Topic Detection. Journal of Chinese Information, 2013,26(6), p121-128
34.
Wei Tang, Yu Hong, Yan-hui Feng, Jian-min Yao, Qiao-ming Zhu.A Novel Method for Parallel Resources Acquisition from Bilingual Web Page.Journal of Computational Information Systems, 2013, 9(6). p2175-2182.
33.
Ma Bin ,Hong Yu,Yang Xuerong,Yao Jianmin, Zhu Qiaoming.Using Event Dependency Cue Inference to Recognize Event Relation. Acta Scientiarum Naturalium Universitatis Pekinensis (ASNUP), 2013, 49(1):109-116.
32.
Hong Yu, Kang Yangyang, Yao Jianmin,Zhou Guodong Zhu Qiaoming.2011.Discovery and Illustration of Novel Optimal Retrieval Result. CCIR 2011 Meeting Paper. Chinese Journal of Computers. 2013,36(3),643-653.
2012

31.
Hong Yu, Cang Yu, Yao Jianmin, Zhou Guodong, Zhu Qiaoming. 2012. Descending Kernel Track of Static and Dynamic Topic Models in Topic Tracking. Journal of Software. 23(5): 1100-1119. [EI]
30.
Hong Yu, Lu Jun, Yao Jianmin, Zhu Qiaoming. 2012. What Reviews are Satisfactory: Novel Features for Automatic Helpfulness Voting. Sigir2012.
29.
Yu Hong,Xiaopei Zhou,Tingting Che,Jianmin Yao,Qiaoming Zhu,Guodong Zhou: Cross-Argument Inference for Implicit Discourse Relation Recognition.In Proceedings of the 21st ACM International Conference on Information and Knowledge Management(CIKM 2012),p295-304
28.
Wei Tang, Yu Hong, Yan-hui Feng, Jian-min Yao, Qiao-ming Zhu Simultaneous Product Attribute Name and Value Extraction with Adaptively Learnt Templates.The 2nd International Conference on Computer Science and Service System (CSSS 2012), p2021-2025
27.
Zhou Xiaopei,Hong Yu, Che Tingting, Yao Jianmin,Zhu Qiaoming. Implicit Discourse Relation Inference Based Parallel Arguments. Computer Applications and Software, 2012, 29(9), p57-61.
26.
Lu Jun,Hong Yu,Yao Jianmin,Zhu Qiaoming.Automatic Reviews Quality Evaluation Based on Global User Intent. Journal of Chinese Information,2012,26(5),p79-87
25.
Yangyang Kang, Yu Hong, Li Yu, Jianmin Yao, Qiaoming Zhu. Divided pretreatment to targets and intentions for query recommendation. NLP&CC2012,Springer CCIS 333, 199-212
2011

24.
Lu Yuqing,Hong Yu, Lu Jun, Yao Jianmin, Zhu Qiaoming. 2011.Context Dependent Word Error Detection and Correction. Journal of Chinese Information Processing. 25(1):85-90.
23.
Sun Changlong,Hong Yu, Ge Yundong, Yao Jianmin, Zhu Qiaoming. 2011.The Translation Mining of the Out of Vocabulary Based on Wikipedia. Journal of Computer Research and Development. 48(6):1067-1076.
22.
Lu Jun,Hong Yu, Yao Jianmin, Zhu Qiaoming. 2011.Automatic Product Review Value Assessment Based on Global User Intent. CCIR2011(recommended to Journal of Chinese Information Processing).
21.
Ma Bin,Hong Yu, Zhang Jianfeng, Yao Jianmin, Zhu Qiaoming. 2011.A Thread-based Two -stage Clustering Method of Microblog Content Topic Detection. CCIR2011(recommended to Journal of Chinese Information Processing).
20.
Feng Yanhui,Hong Yu, Tang Wei, Yao Jianmin, Zhu Qiaoming. 2011.Using HTML Tags to Improve Parallel Resources Extraction. IALP(2011), November, Penang, Malaysia, p255 – 259.
19.
Zhang Jianfeng, Xia Yunqing, Ma Bin, Yao Jianmin, andHong Yu. 2011.Thread Cleaning and Merging for Microblog Topic Detection. In the Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP2011), November, Chiang Mai, Thailand, p589-597.
18.
Hong Yu, Zhang Jianfeng, Ma Bin, Yao Jianmin, Zhou Guodong, and Zhu Qiaoming. 2011. Using Cross-Entity Inference to Improve Event Extraction. In the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), June, Portland, USA, p1127-1136.
17.
Hong Yu, Lu Jun, Yao Jianmin, and Zhu Qiaoming. 2011. Factual or Satisfactory: What Search Results Are Better? In the Proceedings of the 25th Pacific Asia Conference on Language Information and Computing (PACLIC 2011), December, Singapore, p110-119.
2010

16.
Hong Yu, Cai Qingqing, Hua Song, Yao Jianmin, and Zhu Qiaoming. 2010. Negative Feedback: The Forsaken Nature Available for Re-ranking. In the Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), August, Beijing, China, p436-444.
15.
Yan Zhenxiang, Feng Yan-Hui,Hong Yu, Yao Jian-Min:Parallel Sentences Mining From The Web. 2010 JCIS, p1633-1641.
14.
Hua Song,Hong Yu, Zhang Jianfeng , Yao Jianmin, Zhu Qiaoming. 2010.Opposite Re-ranking Based on Relevant Subtopic dispelling. CCIR2010. p237-250
13.
Ge Yundong,Hong Yu, Yao Jianmin, and Zhu Qiaoming. 2010.Improving Web-Based OOV Translation Mining for Query Translation. In the Proceedings of the 6th Asia Information Retrieval Societies Conference (AIRS 2010), December, Taipei, Taiwan, p576–587.
12.
Feng Yanhui,Hong Yu, Yan Zhenxiang, Yao Jianmin, and Zhu Qiaoming. 2010.A Novel Method for Bilingual Web Page Acquisition from Search Engine Web Records. In the Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), August, Beijing, China, p294-302.
11.
Zhang Jianfeng,Hong Yu, Yang Yuehui, Yao Jianmin, Zhu Qiaoming.A Grammar-based Unsupervised Method of Mining Volitive Words. IALP(2010).
10.
Zhang Jianfeng,Hong Yu, Ma Bin, Yao Jianmin, and Zhu Qiaoming. 2010.Chinese Volitive Words Mining. International Journal of Asian Language Processing (IJALP 2010), 20(4): 157-170.
2009

9.
Hong Yu, Zhang Yu, Liu Ting, Li Sheng. 2009. A Temporal Topic Model for New Event Detection. International Journal of Computer Processing of Languages (IJCPOL), 22(1): 1-32.
8.
Hong Yu, Zhang Yu, Liu Ting, Li Sheng. 2009. Incremental Novelty Learning in Adaptive Topic Tracking. International Journal on Asian Language Processing (IALP), 19(1): 13-32.
7.
Yan Zhenxiang, Feng Yan-Hui,Hong Yu, Yao Jian-Min:Parallel Resource Mining From Bilingual Web Pages,2009 CCIR, p513-524.
2008

6.
Hong Yu, Zhang Yu, Fan Jili, Liu Ting, Li Sheng. 2008. New Event Detection Based on Division Comparison of Subtopic. Chinese Journal of Computers. 31(4): 687-695. [EI]
5.
Hong Yu, Zhang Yu, Zheng Wei, Liu Ting, Li Sheng. 2008. Algorithm of Shielding Noises in Information Filtering Based on Distribution of Two-Dimension Similarity Relation. Journal of Software. 19(11): 2887-2898. [EI]
4.
Hong Yu, Zhang Yu, Fan Jili, Liu Ting, Li Sheng. 2008. Chinese Topic Link Detection Based on Semantic Domain Language Model. Journal of Software. 19(9): 2265-2275. [EI]
3.
Zheng Wei, Zhang Yu,Hong Yu, Fan Jili, Liu Ting.Topic Tracking Based on Keywords Dependency Profile. The 4th Asia Information Retrieval Symposium (AIRS). 2008: 129-140. [EI]
2007

2.
Hong Yu, Zhang Yu, Liu Ting, Li Sheng. 2007. Topic Detection and Tracking Review. Journal of Chinese Information Processing. 21(6): 71-87.
1.
Hong Yu, Zhang Yu, Liu Ting, Zheng Wei, Gong Cheng, Li Sheng. 2007. Learning Algorithm of Adaptive Information Filtering Based on Hierarchy Clustering. Journal of Chinese Information Processing. 21(3): 47-53.


1.
Grant: NSFC(Young Scientist), #61003152(Dr. Hong Yu)
Project Name: News topic detection on preference learning
Duration: 2011.01-2013.12
Research funding: 200K RMB
Abstract:   News topic detection, as an important subject in public opinion analysis, is of high value to the monitor, management and regulation of public opinion. Especially, news topic variant detection is of vital importance to the forecast of both abrupt and sensitive topics. At present, no previous work has done on the topic evolution detection and the study on topic evolution pattern using public opinion preference learning. The project will put emphasis on learning collaborative evolution pattern of news topic and preference, along with its machine learning strategy. It will also explore the real time detection of variant anchors, as well as the description rule for topic variant. The project can be divided into four aspects: topic modeling based on temporal event chain, opinion recognition based on volitive words, adaptive learning of collaborative evolution of topic, opinion and real-time topic variant detection. It focus on the structured modeling of dynamic topic integrating temporal attribute, preference evolution description using the strength levels of volitive words, as well as collaborative evolution modeling relying on strength of preference and abrupt event dependency. The project aims to implement the automatic monitor of collaborative evolution of preference and topic in public opinion, as well as effective recognition and forecast of topic variant.

2.
Grant: NSFC(Suzhou International Cooperation Projects), (Dr. Hong Yu)
Project Name: Research on event discourse relation detection in web public opinions
Duration: 2013.01-2016.12
Research funding: 800K RMB
Abstract:   Characters):As an important research task in the cross-field of information extraction and public opinion analysis, Event Discourse Relation Detection (abbr., EDRD) has high practical value in extracting logical relations for nature languages that specially represent events, and mining occurrence cues and development veins of public opinions by using relevant events. However, there are still few researches focusing on the event relation detection, and especially it is still a blank field to use discourse analysis to thoroughly explain and describe the event relation at the semantic level. For this, the project focuses on researching the linguistic regularities of event relation, and based on the discourse analysis, exploring the methods of machine learning and automatically detecting event discourse relation. The research content can be divided into the following four parts: the first is cross-entity inference based event extraction, the second is dynamic topic modeling for cross-discourse relevant event identification, the third is parallel theory based event discourse relation detection, and the last is hierarchical scope establishing for event discourse relation. Especially the project will research the method of identifying shallow event relation under the restriction of global topics, and the mathematical modeling method of detecting event logistical relation based on parallelization identification of event semantics, when given the general characteristics of languages in the process of parallel events forming discourse relations. On the basis, the project finally aims to automatically identify and detect kinds of event logical relations in public opinions, by which to support the prediction of event occurrence and evolution.

3.
Grant: NSFC, (Dr. Hong Yu)
Project Name: Fundamental Theories and Key Technologies on Pseudo Event Relation Detection
Duration: 2014.01-2017.12
Research funding: 800K RMB
Abstract:   Pseudo event relation is a kind of relation which diverges or even deviates from objective laws, generating in the knowledge architecture triggered by subjective thinking and spreading in the procedure of information description and transmission with language as carrier. It includes three characteristics: confusing, ambiguous and illogical relationship. Correspondingly, pseudo event relation detection is an important subject in the cross-domain of natural language understanding and social opinion analysis, having a high research value in resolving the semantics and pragmatics of language description for event relation as well as discerning truth on the relation within social opinion. Current research on the fundamental theories of pseudo event relation has not started yet and corresponding technologies for detecting such event relation has no presence. For this, we build a framework for pseudo event relation detection on the basis of discourse relation understanding with focus on the characteristics of such pseudo event relations, under which, we study various tasks such as relevant event identification, relation cue mining, confusing event relation detection, ambiguous relation detection and illogical relation detection. Especially, we focus on exploring the key techniques on multiple cues fusion based confusing event relation detection, argument-focus co-reference based ambiguous event relation detection, and illogical event relation detection under the restriction of global event network, by which to implement an automatic scheme of identifying and detecting pseudo event relation in social opinion.