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

coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better

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

. 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

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

algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.