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| Investigating the Performance Improvement of Microblog Keyword Extraction by Eye-tracking Data from General-domain Corpus |
| Zhang Chengzhi, Hu Shaohu, Zhang Yingyi |
| Department of Information Management, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094 |
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Abstract Eye-tracking data records the eye trajectory of the viewer when browsing information. Some studies have measured the readers' attention on different words based on eye-tracking data and further added this feature to the microblog keyword extraction model to improve the performance of extraction models. However, the current research only performs keyword extraction based on the total gaze duration of eye-tracking data in the general domain. Moreover, the impact of eye-tracking data on the performance of microblog keyword extraction tasks remains to be fully explored. To this end, the present article comprehensively examines the performance of eye-tracking data of general corpus on microblog keywords from the following aspects of eye movement feature selection: eye movement feature and text feature combination. Simultaneously, due to the large differences in data size between the eye-tracking data and the test data sets, the eye movement characteristics are too sparse and thus impact their effectiveness. Finally, to resolve this issue, an expansion program for eye movement data is proposed.
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Received: 01 June 2020
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