We present a new method for information retrieval using hidden Markov models (HMMs) and relate our experience with this system on the TREC-7 ad hoc task. We develop a general framework for incorporating multiple word generation mechanisms within the same model. We then demonstrate that an extremely simple realization of this model substantially outperforms tf :idf ranking on both the TREC-6 and TREC7 ad hoc retrieval tasks. We go on to present several algorithmic refinements, including a novel method for performing blind feedback in the HMM framework. Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task, and showed extraordinary performance in development experiments on TREC-6.

This paper presents the first-ever results of applying statistical parsing models to the newly-available Chinese Treebank. We have em- ployed two models, one extracted and adapted from BBN's SIFT Sys- tem (Miller et al., 1998) and a TAGbased parsing model, adapted from (Chiang, 2000). On sentences with _40 words, the former model performs at 69% precision, 75% recall, and the latter at 77% precision and 78% recall.

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This paper describes a first attempt at a statistical model for simultaneous syntactic parsing and generalized word-sense disambiguation. On a new data set we have constructed for the task, while we were disappointed not to find parsing improvement over a traditional parsing model, our model achieves a recall of 84.0% and a precision of 67.3% of exact synset matches on our test corpus, where the gold standard has a reported inter-annotator agreement of 78.6%.

In many important document classification tasks, documents may each be associated with multiple class labels...

The Sydney Language Independent Named Entity Recogniser and Classifier (SLINERC) is a multi-stage system for the recognition and classification of named entities. Each stage uses a decision graph learner to combine statistical features with results from prior stages. Earlier stages are focused upon entity recognition, the division of non-entity terms from entities. Later stages concentrate on the classification of these entities into the desired classes. The best overall f-values are 73.92 and 71.36 for the Spanish and Dutch datasets, respectively.

We present a new method for information retrieval using hidden Markov models (HMMs). We develop a general framework for incorporating multiple word generation mechanisms within the same model. We then demonstrate that an extremely simple realization of this model substantially outperforms standard tf:idf ranking on both the TREC-6 and TREC-7 ad hoc retrieval tasks. We go on to present a novel method for performing blind feedback in the HMM framework, a more complex HMM that models bigram production, and several other algorithmic re nements. Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task.