Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised and supervised clustering for topic tracking
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Topic detection and tracking evaluation overview
Topic detection and tracking
A month to topic detection and tracking in Hindi
ACM Transactions on Asian Language Information Processing (TALIP)
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Language-specific models in multilingual topic tracking
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Comparing Knowledge Sources for Nominal Anaphora Resolution
Computational Linguistics
The influence of minimum edit distance on reference resolution
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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This article focuses on overt pronouns which are related to a topic and an event in news stories, and studies issues on the effect of their resolution in topic tracking. The antecedent of the pronoun is identified by using three linguistic features, morphological, syntactic and semantic knowledge. The morphological cues are part-of-speech information including named entities. Syntactic and semantic information is verbs and their subcategorization frames with selectional preferences. They are derived from the WordNet and VerbNet. The results on the TDT3 English show the usefulness of the overt pronoun resolution, especially for a small number of positive training data.