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Implicit discourse relation recognition by selecting typical training examples
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In: http://aclweb.org/anthology/C/C12/C12-1168.pdf (2012)
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Extractive Summarization using Supervised and Semi-supervised learning
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In: http://www.cs.fiu.edu/%7Elli003/Sum/Coling/2008/C08-1124.pdf (2008)
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Exploiting the role of position feature in chinese relation extraction
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In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/540_paper.pdf (2008)
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A phoneticbased approach to chinese chat text normalization
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In: http://acl.ldc.upenn.edu/P/P06/P06-1125.pdf (2006)
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| 5 |
Automatic Chinese Aspectual Classification Using Linguistic Indicators
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In: http://www.icis.ntu.edu.sg/scs-ijit/1204/1204_11.pdf (2006)
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| 6 |
Combining linguistic features with weighted bayesian classifier for temporal reference processing
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In: http://www.aclweb.org/anthology-new/C/C04/C04-1101.pdf (2004)
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| 7 |
Applying machine learning to Chinese temporal relation resolution
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In: http://acl.ldc.upenn.edu/p/p04/p04-1074.pdf (2004)
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| 8 |
Extractive Summarization using Inter- and Intra- Event Relevance
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In: http://www.aclweb.org/anthology-new/P/P06/P06-1047.pdf
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Abstract:
Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. Each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. In this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of internal association, semantic relatedness, distributional similarity and named entity clustering. We then apply PageRank ranking algorithm to estimate the significance of an event for inclusion in a summary from the event relevance derived. Experiments on the DUC 2001 test data shows that the relevance of the named entities involved in events achieves better result when their relevance is derived from the event terms they associate. It also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like Word-Net. 1.
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URL: http://www.aclweb.org/anthology-new/P/P06/P06-1047.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.4453
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| 9 |
PolyU at MOAT in NTCIR-8
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings8/NTCIR/14-NTCIR8-MOAT-ZhangW.pdf
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| 10 |
Interaction between Lexical Base and Ontology with Formal Concept Analysis
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In: http://www.cs.brandeis.edu/~marc/misc/proceedings/lrec-2006/pdf/796_pdf.pdf
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| 11 |
Opinion Annotation in On-line Chinese Product Reviews
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In: http://lrec-conf.org/proceedings/lrec2008/pdf/415_paper.pdf
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| 12 |
A Cluster-Sensitive Graph Model for Query-Oriented Multi-document Summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/ECIR/2008/1.pdf
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| 13 |
Query-Sensitive Mutual Reinforcement Chain and Its Application in Query-Oriented Multi-Document Summarization
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In: http://www.cs.fiu.edu/%7Elli003/Sum/SIGIR/2008/4.pdf
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| 14 |
Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization 1
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In: http://aclweb.org/anthology-new/C/C10/C10-1016.pdf
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| 15 |
Fine-Grained Classification of Named Entities by Fusing Multi-features ABSTRACT
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In: http://aclweb.org/anthology/C/C12/C12-2068.pdf
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| 16 |
Sequential Summarization: A New Application for Timely Updated Twitter Trending Topics
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In: http://aclweb.org/anthology/P/P13/P13-2101.pdf
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| 17 |
The 6th Workshop on Asian Languae Resources, 2008 Preliminary Chinese Term Classification for Ontology Construction
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In: http://aclweb.org/anthology/I/I08/I08-7003.pdf
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| 19 |
Linguistics
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In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2007/p79-ouyang.pdf
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