solution to organizing large document corpus. As a sub-field of data mining, text mining is to discover useful information from written resources. Text clustering is one of topics in text mining, which is to find out the groups information from the text documents and cluster these documents into the most

for testing and training. We have achieved considerable progress recently by taking advantage of a new source of testing and training materials. Rather than depending on small amounts of hand-labeled text, we have been making use of relatively large amounts of parallel text, text such as the Canadian Hansards

and comparison of the adequacy of parsing models. In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus 1 consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989

This paper investigates the use of a large corpus for the training of a Broadcast News speech recognizer. A vast body of speech recognition algorithms and mathematical machinery is aimed at smoothing estimates toward accurate modeling with scant amounts of data. In most cases, this research

We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achiev

. In addition, current computing infrastructure is unable to deal with very large corpuses directly. Thus, new algorithms are needed to seek parallel solutions and utilize more processing power to solve this problem. In this paper we propose a new algorithm that partitions a large collection of documents (a

This paper describes an experiment on the identification of frozen sentences (or verbal idioms) from European Portuguese on large corpus of journalistic text. It aims at identifying the main difficulties (or shortcomings) resulting from the intersection of linguistic information encoded

While knowledge is viewed by many as an asset, it is often difficult to locate particular items within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work

the maximum likelihood alignment of sentences. It is remarkable that such a simple approach works as well as it does. An evaluation was performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzerland (UBS) in English, French, and German. The method correctly aligned all

Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine