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Ziheng Lin 的引文(23) 排序方式:
The Biomedical Discourse Relation Bank  
Background  Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text cor......
BMC Bioinformatics  2011
0次引用 0 0
Semi-supervised Discourse Relation Classification with Structural Learning  
Abstract. The corpora available for training discourse relation classifiers are annotated using a general set of discourse relations. However, for certain applications, custom discourse relations are ......
Conference on Intelligent Text Processing and Computational Linguistics  2011
0次引用 0 0
The Biomedical Discourse Relation Bank  
Background  Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text cor......
BMC Bioinformatics  2011
0次引用 0 0
Improving Update Summarization by Revisiting the MMR Criterion  
This paper describes a method for multi-document update summarization that relies on a double maximization criterion. A Maximal Marginal Relevance like criterion, modified and so called Smmr, is used ......
Computing Research Repository  2010
0次引用 0 0
Statistical Physics for Natural Language Processing  
In this paper we study the {\sc Enertex} model that has been applied to fundamental tasks in Natural Language Processing (NLP) including automatic document summarization and topic segmentation. The mo......
Computing Research Repository  2010
0次引用 0 0
A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension  
u-tokyo.ac.jp Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human annotators to beforehand create an extensive training corpus, whi......
Empirical Methods in Natural Language Processing  2010
3次引用 0 0
Predicting Discourse Connectives for Implicit Discourse Relation Recognition  
Existing works indicate that the absence of explicit discourse connectives makes it difficult to recognize implicit discourse relations. In this paper we attempt to overcome this difficulty for implic......
International Conference on Computational Linguistics  2010
3次引用 0 0
A PDTB-Styled End-to-End Discourse Parser  
We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments......
Computing Research Repository  2010
1次引用 0 0
Using entity features to classify implicit discourse relations  
We report results on predicting the sense of implicit discourse relations between adjacent sentences in text. Our investigation concentrates on the association between discourse relations and properti......
 2010
2次引用 0 0
XRCE’s participation to ImageCLEF 2008  
This year, our participation to ImageCLEF 2008 (Photo Retrieval sub-task) was motivated by trying to address three different problems: visual concept detection and its exploitation in a retrieval cont......
Working Notes of the 2008 CLEF Workshop  2008
1次引用 0 0

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