Categorization of Unorganized Text Corpora for better Domain-Specific Language Modeling

Categorization of Unorganized Text Corpora for better Domain-Specific Language Modeling

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This paper describes the process of categorization of unorganized text data gathered from the Internet to the in-domain and out-of-domain data for better domain-specific language modeling and speech recognition.An algorithm for text categorization and topic detection based Stovetop Kettle on the most frequent key phrases is presented.In this scheme, each document entered into the process of text categorization is represented by a vector space model with term weighting based on computing the term frequency and inverse document frequency.

Text documents are then classified to the in-domain and out-of-domain data Vertical Rectangle Wooden Plaque automatically with predefined threshold using one of the selected distance/similarity measures comparing to the list of key phrases.The experimental results of the language modeling and adaptation to the judicial domain show significant improvement in the model perplexity about 19 % and decreasing of the word error rate of the Slovak transcription and dictation system about 5,54 %, relatively.