Word association norms, mutual information, and lexicography
Computational Linguistics
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Using mutual information to resolve query translation ambiguities and query term weighting
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Using query contexts in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Comparing different units for query translation in Chinese cross-language information retrieval
Proceedings of the 2nd international conference on Scalable information systems
An Automatic Online News Topic Keyphrase Extraction System
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Effective annotation and search for video blogs with integration of context and content analysis
IEEE Transactions on Multimedia - Special issue on integration of context and content
Using term relation in context sensitive information retrieval
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Using Markov chains to exploit word relationships in information retrieval
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
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Co-occurrence analysis has been used to determine related words or terms in many NLP-related applications such as query expansion in Information Retrieval (IR). However, related words are usually determined with respect to a single word, without relevant information for its application context. For example, the word "programming" may be considered to be strongly related to "Java", and applied inappropriately to expand a query on "Java travel". To solve this problem, we propose to add another context word in the relation to specify the appropriate context of the relation, leading to term relations of the form "(Java, travel) → Indonesia". The extracted relations are used for query expansion in IR. Our experiments on several TREC collections show that this new type of context-dependent relations performs much better than the traditional co-occurrence relations.