On-Line Topic Segmentation Using Convolutional Neural Networks


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 11, pp. 585-592, Nov. 2016
10.3745/KTSDE.2016.5.11.585,   PDF Download:
Keywords: Topic Segmentation, Convolutional Neural Network
Abstract

A topic segmentation module is to divide statements or conversations into certain topic units. Until now, topic segmentation has progressed in the direction of finding an optimized set of segments for a whole document, considering it all together. However, some applications need topic segmentation for a part of document which is not finished yet. In this paper, we propose a model to perform topic segmentation during the progress of the statement with a supervised learning model that uses a convolution neural network. In order to show the effectiveness of our model, we perform experiments of topic segmentation both on-line status and off-line status using C99 algorithm. We can see that our model achieves 17.8 and 11.95 of Pk score, respectively,


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Cite this article
[IEEE Style]
G. H. Lee and K. J. Lee, "On-Line Topic Segmentation Using Convolutional Neural Networks," KIPS Transactions on Software and Data Engineering, vol. 5, no. 11, pp. 585-592, 2016. DOI: 10.3745/KTSDE.2016.5.11.585.

[ACM Style]
Gyoung Ho Lee and Kong Joo Lee. 2016. On-Line Topic Segmentation Using Convolutional Neural Networks. KIPS Transactions on Software and Data Engineering, 5, 11, (2016), 585-592. DOI: 10.3745/KTSDE.2016.5.11.585.