Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NS07040)National Institutes of Health (Grant 5 R01 NS04332)National Science Foundation (Grant 1ST 80-17599)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0290

Speech Communication

Contains table of contents for Part IV, table of contents for Section 1 and reports on five research projects.Apple Computer, Inc.C.J. Lebel FellowshipNational Institutes of Health (Grant T32-NS07040)National Institutes of Health (Grant R01-NS04332)National Institutes of Health (Grant R01-NS21183)National Institutes of Health (Grant P01-NS23734)U.S. Navy / Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Office of Naval Research (Contract N00014-82-K-0727

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar

A Patent Search and Classification System

We present a system for searching and classifying U.S. patent documents, based on Inquery. Patents are distributed through hundreds of collections, divided up by general area. The system selects the best collections for the query. Users can search for patents or classify patent text. The user interface helps users search in fields without requiring the knowledge of Inquery query operators. The system includes a unique "phrase help" facility, which helps users find and add phrases and terms related to those in their query. Introduction At the Center for Intelligent Information Retrieval (CIIR) at the University of Massachusetts we are working with the U.S. Patent and Trademark Office (USPTO) on a project involving the retrieval and classification of U.S. Patent texts, patent images, and trademark images. This paper describes a web-based system for the retrieval and classification of patent text that we have implemented for the USPTO. Notable features of the system include: . A collect..

Improving stemming for arabic information retrieval: Light stemming and co-occurrence analysis

Arabic, a highly inflected language, requires good stemming for effective information retrieval, yet no standard approach to stemming has emerged. We developed several light stemmers based on heuristics and a statistical stemmer based on co-occurrence for Arabic retrieval. We compared the retrieval effectiveness of our stemmers and of a morphological analyzer on the TREC-2001 data. The best light stemmer was more effective for cross-language retrieval than a morphological stemmer which tried to find the root for each word. A repartitioning process consisting of vowel removal followed by clustering using co-occurrence analysis produced stem classes which were better than no stemming or very light stemming, but still inferior to good light stemming or morphological analysis

Some Issues in the Automatic Classification of U.S. Patents

The classification of U.S. patents poses some special problems due to the enormous size of the corpus, the size and complex hierarchical structure of the classification system, and the size and structure of patent documents. The representation of the complex structure of documents has not received a great deal of previous attention, but we have found it to be an important factor in our work. We are exploring ways to use this structure and the hierarchical relations among patent subclasses to facilitate the classification of patents. Our approach is to derive a vector of terms and phrases from the most important parts of the patent to represent each document. We use both k-nearest-neighbor classifiers and Bayesian classifiers. The k-nearest-neighbor classifier allows us to represent the document structure using the query operators in the Inquery information retrieval system. The Bayesian classifiers can use the hierarchical relations among patent subclasses to select closely related neg..

Statistical transliteration for English-Arabic cross language information retrieval

Out of vocabulary (OOV) words are problematic for cross language information retrieval. One way to deal with OOV words when the two languages have different alphabets, is to transliterate the unknown words, that is, to render them in the orthography of the second language. In the present study, we present a simple statistical technique to train an English to Arabic transliteration model from pairs of names. We call this a selected n-gram model because a two-stage training procedure first learns which n-gram segments should be added to the unigram inventory for the source language, and then a second stage learns the translation model over this inventory. This technique requires no heuristics or linguistic knowledge of either language. We evaluate the statistically-trained model and a simpler hand-crafted model on a test set of named entities from the Arabic AFP corpus and demonstrate that they perform better than two online translation sources. We also explore the effectiveness of these systems on the TREC 2002 cross language IR task. We find that transliteration either of OOV named entities or of all OOV words is an effective approach for cross language IR