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- W2156888586 abstract "We describe a new probabilistic approach in the language modeling framework that captures adaptively the local term dependencies in documents. The new model works by boosting scores of documents that contain topic-specific local dependencies and exhibits the behavior of the unigram model in the absence of such dependencies. Contributions of the current work include adapting van Rijsbergen’s [14] work in the classical probabilistic framework to the language modeling framework and adaptive modeling of withinsentence dependencies. We evaluated our model on two different tasks of the Topic Detection and Tracking (TDT) research program, namely Story Link Detection and Topic Tracking. Our results show that the Local dependency language model consistently outperforms the basic unigram model on both the tasks." @default.
- W2156888586 created "2016-06-24" @default.
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- W2156888586 date "2003-01-01" @default.
- W2156888586 modified "2023-09-26" @default.
- W2156888586 title "An Adaptive Local Dependency Language Model: Relaxing the NaBayes' Assumption" @default.
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