Abstract
This article presents a general method to use information retrieved from the Latent Dirichlet Allocation (LDA) topic model for Text Segmentation: Using topic assignments instead of words in two well-known Text Segmentation algorithms, namely TextTiling and C99, leads to significant improvements. Further, we introduce our own algorithm called TopicTiling, which is a simplified version of TextTiling (Hearst, 1997). In our study, we evaluate and optimize parameters of LDA and TopicTiling. A further contribution to improve the segmentation accuracy is obtained through stabilizing topic assignments by using information from all LDA inference iterations. Finally, we show that TopicTiling outperforms previous Text Segmentation algorithms on two widely used datasets, while being computationally less expensive than other algorithms.
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 callMore From: Journal for Language Technology and Computational Linguistics
Disclaimer: 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.
