Lexical ambiguity and information retrieval
ACM Transactions on Information Systems (TOIS)
Towards building contextual representations of word senses using statistical models
Corpus processing for lexical acquisition
Disambiguating highly ambiguous words
Computational Linguistics - Special issue on word sense disambiguation
Use of dependency tree structures for the microcontext extraction
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
The Current Status of the Prague Dependency Treebank
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Use of dependency tree structures for the microcontext extraction
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
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This paper focuses especially on two problems that are crucial for retrieval performance in information retrieval (IR) systems: the lack of information caused by document pre-processing and the difficulty caused by homonymous and synonymous words in natural language. Author argues that traditional IR methods, i. e. methods based on dealing with individual terms without considering their relations, can be overcome using natural language processing (NLP). In order to detect the relations among terms in sentences and make use of lemmatisation and morphological and syntactic tagging of Czech texts, author proposes a method for construction of dependency word microcontexts fully automatically extracted from texts, and several ways how to exploit the microcontexts for the sake of increasing retrieval performance.