q q q q qTo summarize is to reduce in complexity, and hence in length, while retaining some of the essential qualities of the original. This paper focusses on document extracts, a particular kind of computed document summary.Document extracts consisting of roughly 20% of the original cart be as informative as the full text of a document, which suggests that even shorter extracts may be useful indicative summmies.The trends in our results are in agreement with those of Edmundson who used a subjectively weighted combination of features as opposed to training the feature weights using a corpus.We have developed a trainable summarization program that is grounded in a sound statistical framework.-

We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment.

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The paper describes an algorithm that employs English and French text taggers to associate noun phrases in an aligned bilingual corpus. The taggets provide part-of-speech categories which are used by finite-state recognizers to extract simple noun phrases for both languages. Noun phrases are then mapped to each other using an iterative re-estimation algorithm that bears similarities to the Baum-Welch algorithm which is used for training the taggers. The algorithm provides an alternative to other approaches for finding word correspondences, with the advantage that linguistic structure is incorporated. Improvements to the basic algorithm are described, which enable context to be accounted for when constructing the noun phrase mappings.

In this paper we demonstrate that speech recognition can be effectively applied to information retrieval (IR) applications. Our system exploits the fact that the intended words of a spoken query tend to co-occur in text documents in close proximity whereas word combinations that are the result of recognition errors are usually not semantically correlated and thus do not appear together. Termed "Semantic Co-occurrence Filtering" this enables the system to simultaneously disambiguate word hypotheses and find relevant text for retrieval. The system is built by integrating standard IR and speech recognition techniques. An evaluation of the system is preseated and we discuss several refinements to the functionality.