tailieunhanh - Báo cáo khoa học: "Knowledge-based Automatic Topic Identification"

As the first step in an automated text summarization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts, we use the hierarchical concept taxonomy WordNet. By setting appropriate cutoff values for such parameters as concept generality and child-to-parent frequency ratio, we control the amount and level of generality of concepts extracted from the text. | Knowledge-based Automatic Topic Identification Chin-Yew Lin Department of Electrical Engineering System University of Southern California Los Angeles CA 90089-2562 USA chinyew@ Abstract As the first step in an automated text summarization algorithm this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts we use the hierarchical concept taxonomy WordNet. By setting appropriate cutolf values for such parameters as concept generality and child-to-parent frequency ratio we control the amount and level of generality of concepts extracted from the text. 1 1 Introduction As the amount of text available online keeps growing it becomes increasingly difficult for people to keep track of and locate the information of interest to them. To remedy the problem of information overload a robust and automated text summarizer or information extrator is needed. Topic identification is one of two very important steps in the process of summarizing a text the second step is summary text generation. A topic is a particular subject that we write about or discuss. Sinclair et al. 1987 . To identify the topics of texts Information Retrieval IR researchers use word frequency cue word location and title-key word techniques Paice 1990 . Among these techniques only word frequency counting can be used robustly across different domains the other techniques rely on stereotypical text structure or the functional structures of specific domains. Underlying the use of word frequency is the assumption that the more a word is used in a text the more important it is in that text. This method 1This research was funded in part by under order number 8073 issued as Maryland Procurement Contract MDA904-91-C-5224 and in part by the National Science Foundation Grant No. MIP 8902426. recognizes only the literal word forms and nothing else. Some morphological processing .

• Báo cáo khoa học
• Knowledge based Automatic Topic Identification
• Chin Yew Lin
• Long Papers
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