Using social annotations to smooth the language model for IR

Authors:
Shengliang Xu;Shenghua Bao;Yong Yu;Yunbo Cao
Affiliations:
APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University;APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University;APEX Data & Knowledge Management Lab, Shanghai Jiao Tong University;Microsoft Research Asia, Beijing, P.R. China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

Citing 10
Cited 2

A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval

Proceedings of the eighth international conference on Information and knowledge management
Exploiting syntactic structure of queries in a language modeling approach to IR

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A study of smoothing methods for language models applied to information retrieval

ACM Transactions on Information Systems (TOIS)
Cluster-based retrieval using language models

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for the semantic web

Proceedings of the 15th international conference on World Wide Web
Using annotations in enterprise search

Proceedings of the 15th international conference on World Wide Web
Ontologies are us: a unified model of social networks and semantics

ISWC'05 Proceedings of the 4th international conference on The Semantic Web
LSM: language sense model for information retrieval

WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management

A new vector space model exploiting semantic correlations of social annotations for web page clustering

WAIM'11 Proceedings of the 12th international conference on Web-age information management
Optimizing ranking method using social annotations based on language model

Artificial Intelligence Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the paper, we present an exploration of using social annotations provided by the Web 2.0 sites (such as Del.icio.us) in helping web search. More specifically, we consider using the social annotations as an additional resource to strengthen existing smoothing methods for the language model for IR. The social annotations can benefit the smoothing of language model in two aspects: 1) the annotations themselves can serve as the summaries of the web pages given by the users; 2) the annotations can be seen as the links of the web pages sharing the same annotations. We propose three smoothing methods, addressing the two aspects and their combination, respectively. We call the new language model of using the proposed smoothing methods 'Language Annotation Model (LAM). Preliminary experimental results show that LAM significantly outperforms the traditional language models.