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1 Boolean retrieval 1 2 The term vocabulary and postings lists 19 3 Dictionaries and tolerant retrieval 49 4 Index construction 67 5 Index compression 85 6 Scoring, term weighting and the vector space model 109 7 Computing scores in a complete search system 135 8 Evaluation in information retrieval 151 9 Relevance feedback and query expansion 177 10 XML(More)
In 1993, Eugene Charniak published a slim volume entitled Statistical Language Learning. At the time, empirical techniques to natural language processing were on the rise — in that year, Computational Linguistics published a special issue on such methods — and Charniak's text was the first to treat the emerging field. Nowadays, the revolution has become the(More)
In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization: linear discriminant analysis, logistic regression, and neural networks. We(More)
Representations for semantic information about words are necessary for many applications of neural networks in natural language processing. This paper describes an efficient, corpus-based method for inducing distributed semantic representations for a large number of words (50,000) from lexical coccurrence statistics by means of a large-scale linear(More)
This paper presents a method for inducing the parts of speech of a language and part-of-speech labels for individual words from a large text corpus. Vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. A dimen-sionality reduction creates a space representing the syntactic categories of unambiguous(More)