HIT-LTRC at TREC 2010 blog track: Faceted blog distillation

Jinfeng Yang; Xishuang Dong; Yi Guan; Chengzhen Huang; Sheng Wang

HIT-LTRC at TREC 2010 blog track: Faceted blog distillation

Jinfeng Yang^*
, Xishuang Dong
, Yi Guan
, Chengzhen Huang
, Sheng Wang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper describes our participation in the faceted blog distillation task at Blog Track 2010. In our approach, indri toolkit is applied for basic topic relevance retrieval. Then the Maximum Entropy (ME) model is adopted to judge the relevance of each blog to specified facet. Feed faceted relevance is calculated by integrating the average relevance of all blogs within a feed and the average relevance of the most relevant N blogs. Two implementations are applied to calculate feed faceted relevance. Experiental results on Blogs08 dataset show the effectiveness of our approach.

Original language	English
Journal	NIST Special Publication
State	Published - 2010
Externally published	Yes
Event	19th Text REtrieval Conference, TREC 2010 - Gaithersburg, MD, United States Duration: 16 Nov 2010 → 19 Nov 2010

Cite this

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title = "HIT-LTRC at TREC 2010 blog track: Faceted blog distillation",

abstract = "This paper describes our participation in the faceted blog distillation task at Blog Track 2010. In our approach, indri toolkit is applied for basic topic relevance retrieval. Then the Maximum Entropy (ME) model is adopted to judge the relevance of each blog to specified facet. Feed faceted relevance is calculated by integrating the average relevance of all blogs within a feed and the average relevance of the most relevant N blogs. Two implementations are applied to calculate feed faceted relevance. Experiental results on Blogs08 dataset show the effectiveness of our approach.",

author = "Jinfeng Yang and Xishuang Dong and Yi Guan and Chengzhen Huang and Sheng Wang",

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journal = "NIST Special Publication",

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AU - Guan, Yi

AU - Huang, Chengzhen

AU - Wang, Sheng

PY - 2010

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N2 - This paper describes our participation in the faceted blog distillation task at Blog Track 2010. In our approach, indri toolkit is applied for basic topic relevance retrieval. Then the Maximum Entropy (ME) model is adopted to judge the relevance of each blog to specified facet. Feed faceted relevance is calculated by integrating the average relevance of all blogs within a feed and the average relevance of the most relevant N blogs. Two implementations are applied to calculate feed faceted relevance. Experiental results on Blogs08 dataset show the effectiveness of our approach.

AB - This paper describes our participation in the faceted blog distillation task at Blog Track 2010. In our approach, indri toolkit is applied for basic topic relevance retrieval. Then the Maximum Entropy (ME) model is adopted to judge the relevance of each blog to specified facet. Feed faceted relevance is calculated by integrating the average relevance of all blogs within a feed and the average relevance of the most relevant N blogs. Two implementations are applied to calculate feed faceted relevance. Experiental results on Blogs08 dataset show the effectiveness of our approach.

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Y2 - 16 November 2010 through 19 November 2010

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HIT-LTRC at TREC 2010 blog track: Faceted blog distillation

Abstract

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