xcit'ed

Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked termpairs and term clusters, analyses of the correlation among the association measures using Pearson¡Çs correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule¡Çs coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as x2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the x2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule¡Çs Y seem to overestimate rare terms.

well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in [1] and [2]) based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term¡Çs membership in a particular thesaurus class, is found not to be useful in distinguishing between thesaurus classes (i.e., in differentiating a ¡Ègood¡É from an ¡Èindifferent¡É or ¡Èpoor¡É thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.

Abstract. This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semantic information available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against which words are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.

In this paper we present a method to group adjectives according to their meaning, as a first step towards the automatic identification of adjectival scales. We discuss the properties of adjectival scales and of groups of semantically related adjectives and how they imply sources of linguistic knowledge in text corpora. We describe how our system exploits this linguistic knowledge to compute a measure of similarity between two adjectives, using statistical techniques and without having access to any semantic information about the adjectives. We also show how a clustering algorithm can use these similarities to produce the groups of adjectives, and we present results produced by our system for a sample set of adjectives. We conclude by presenting evaluation methods for the task at hand, and analyzing the significance of the results obtained.

We propose a method to build thesauri on the basis of grammatical relations. The proposed method constructs thesauri by using a hierarchical clustering algorithm. An important point in this paper is the claim that thesauri in order to be efficient need to take (surface) case information into account. We refer to the thesauri as "relation-based thesaurus (RBT)." In the experiment, four RBTs of Japanese nouns were constructed from 26,023 verb-noun co-occurrences, and each RBT was evaluated by objective criteria. The experiment has shown that the RBTs have better properties for selectional restriction of case frames than conventional ones.

The term word association is used in a very particular sense in the p!ycholinguistic literature. (Generally speaking, subjects respond quicker than normal to the word "nurse" if it follows a highly associated word such as "doctor.") We will extend the term to provide the basis for a statistical description of a variety of interesting linguistic phenomena, ranging from semantic rehtions of the doctor/nurse type (content word/content word) to lexico-syntactlc co-occurrence constraints between

Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent benefits for retrieval performance, and it is difficult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in INQUERY to evaluate different tyes of association thesauri, and thesauri constructed for a variety of collections.

Abstract: Context is used in many NLP systems as an indicator of a term's syntactic and semantic function. The accuracy of the system is dependent on the quality and quantity of contextual information available to describe each term. However, the quantity variable is no longer fixed by limited corpus resources. Given fixed training time and computational resources, it makes sense for systems to invest time in extracting high quality contextual information from a fixed corpus. However, with an effectively limitless quantity of text available, extraction rate and representation size need to be considered. We use thesaurus extraction with a range of context extracting tools to demonstrate the interaction between context quantity, time and size on a corpus of 300 million words.

This paper describes a method to construct related terms thesauri automatically based on Web information. We utilize Web search engine to obtain word co-occurrence information and propose a new efficient similarity metrics applying \chi^2 value to solve problems of the existing methods. We also introduce a new method to identify related terms using word-clustering. We do word-clustering on that associative network to identify related terms using latest clustering methods, "Newman method". We make evaluations and show the effectiveness of our approach using sets of related terms extracted from a corpus and a current thesaurus.

Motivation. How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Results. We propose a minimum redundancy – maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 5 gene expression data sets: NCI, Lymphoma, Lung, Leukemia and Colon. Improvements are observed consistently among 4 classification methods: Na¾­¥Áve Bayes, Linear discriminant analysis, Logistic regression and Support vector machines. Supplimentary: The top 60 MRMR genes for each of the dataset are listed in http://www.nersc.gov/~cding/MRMR/

To improve the proficiency of text processing such as information retrieval or text mining, it is necessary to construct a synonym dictionary, but it is very tiresome to make it by hands. In some fields, such as aviation, synonym nouns are mixed with kanji/hiragana, katakana, alphabet and their abbreviations. As new words always come to be used, the dictionary update is a big issue. In this paper, we propose a tool for constructing a synonym dictionary. The system will return synonym candidates against a query. A synonym can be easily registered in dictionary by looking the synonym candidates. We experimented the system performance by aviation pilot report and evaluated it by average precision.

Abstract: A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification.

Abstract. Is it possible to discover semantic term relations useful for thesauri without any semantic information? Yes, it is. A recent approach for automatic thesaurus construction is based on explicit linguistic knowledge, i.e. a domain independent parser without any semantic component and implicit linguistic knowledge contained in large amounts of real world texts. Such texts include implicitly the linguistic, especially semantic knowledge that the authors needed for formulating their texts. This article explains how implicit semantic knowledge can be transformed to an explicit one. Evaluations of quality and performance of the approach are very encouraging.

Abstract: Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.