This paper proposes a new term weighting method called weighted inverse document frequency (WIDF). As its name indicates, WIDF is an extension of IDF (inverse document frequency) to incorporate the term frequency over the collection of texts. WIDF of a term in a text is given by dividing the frequency of the term in the text by the sum of the frequency of the term over the collection of texts. WIDF is applied to the text categorization task and proved to be superior to the other methods. The improvement of accuracy on IDF is 7.4% at the maximum.

This dissertation examines the role of salience in natural language generation (NLG). The salience of an entity, in intuitive terms, refers to its prominence, and is interpreted as a measure of how well an entity stands out from other entities and biases the preference of the generator in selecting words and complex constructs. Through an analysis of previous work in diverse disciplines, we show the variety of salience effects in NLG. Next, we classify several important determinants of salience, corresponding to different factors contributing to salience. We then delineate two theoretically-significant categories: canonical salience and instantial salience. The former is characterized as a built-in preference in the general conceptual- and linguistic knowledge of the speaker. The latter refers to the salience of specific objects in the context of NLG, and may accrue through such determinants as vividness and recency of mention. Psycholinguistic results of Osgood and Bock are highligh...

Case-based reasoning(CBR) is a topic that becomes more and more important and recently has been used for planning, design, etc, of applications in industry and business domains. In case-based reasoning, a problem is solved by recognizing its similarity to a known problem (i.e., a case) and then adapting the corresponding solution to solve the new problem. Consequently similarity metrics plays a central role in CBR. This paper proposes a method of similarity metrics based on cases being represented by structured representations. First, we define the similar correspondence and the degree of similarity between graphs. Because the problem of computing them can be translated in combinational optimization problem, we can apply genetic algorithms which can find near optimal solutions fastly. Finally, we will show the results of our simulation. INTRODUCTION Case-based reasoning(CBR) is a topic that becomes more and more important and recently has been used for planning, design, etc, of appli...

Similarity metrics is a central issue in automatic reasoning system, especially in case-based reasoning and analogical reasoning. This paper proposes a method of similarity metrics by focusing the similarity of features of problems which are represented by frame knowledge expressions. For retrieving the most similar previous case and adapting it in solving new problem, we think that similarity metrics necessitates to provide the retrieving criterion and the information of adaptation. In our method, we assign a degree of similarity as a retrieving criterion between the features of new problem and past one, and using fuzzy integration to calculate the degree of similarity by comparing target frame which presents the features of new problem with source frame which presents the features of past problem. While calculating the degree of similarity, we also make a comparative frame which includes the information of matching source frame to target frame for adaptation. Finally we show some sim...