Abstract
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 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 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.
Highlights
One of the key problems of the text data gathered from the Internet is their thematic heterogeneity
In the case of domain-specific speech recognition and statistical language modeling, these unorganized text data bring into the process of training language models many ambiguities caused by the overestimating such n-gram probabilities that are typically unrelated with the area, in which the speech recognition is performed
Contemporary text categorization is usually based on topic detection with key word identification for categorization of text data into predefined domains [3] or text document clustering based on measuring similarity between two or more documents [4], [5] with using iterative or hierarchical clustering algorithms [6]
Summary
One of the key problems of the text data gathered from the Internet is their thematic heterogeneity. Contemporary text categorization is usually based on topic detection with key word identification for categorization of text data into predefined domains [3] or text document clustering based on measuring similarity between two or more documents [4], [5] with using iterative or hierarchical clustering algorithms [6] Based on this knowledge, we propose an algorithm for text categorization, which classifies short segments (blocks of texts or paragraphs) from unorganized text corpora to the in-domain and out-of-domain data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
