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’s 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’s 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’s Y seem to overestimate rare terms.

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

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: Na将チve 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/

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: 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.