通用语料的眼动数据对微博关键词抽取的性能提升探究

doi:10.3772/j.issn.1000-0135.2021.04.005

情报学报

2021, Vol. 40

Issue (4): 375-386 DOI: 10.3772/j.issn.1000-0135.2021.04.005

情报分析方法与技术

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通用语料的眼动数据对微博关键词抽取的性能提升探究

章成志, 胡少虎, 张颖怡

南京理工大学经济管理学院信息管理系，南京 210094

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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摘要眼动数据记录了浏览者在浏览信息时的眼球轨迹，已有研究依据眼动数据度量阅读者在不同单词上的注意力强弱，并进一步将该特征加入微博关键词抽取模型中，从而提高抽取模型的性能。然而，目前的微博关键词抽取模型仅考虑通用领域眼动数据的总注视时长这一特征，尚未全面探究眼动数据对微博关键词抽取任务性能的影响。因此，本文将从眼动特征的选择、眼动特征与文本特征的组合这两个方面，全面考察通用语料的眼动数据对微博关键词抽取任务性能的影响。同时，由于眼动数据集与测试数据集在数据规模上相差较大，使得眼动特征过于稀疏，进而影响其作用的发挥，本文提出了一个眼动数据的扩充方案用于解决这一问题。

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关键词 ：眼动数据, 关键词抽取, 序列标注, 深度学习, 注意力机制

收稿日期: 2020-06-01

基金资助:国家自然科学基金面上项目“基于学术文献全文内容的细粒度算法实体抽取与评估研究”(72074113)；国家社会科学基金重大项目“面向三大公共数字文化工程资源融合的多语言信息组织与检索研究”（19ZDA341）。

作者简介: 章成志，男，1977年生，博士，教授，博士生导师，主要研究领域为信息组织、信息检索、数据挖掘及自然语言处理，E-mail：zhangcz@njust.edu.cn；胡少虎，男，1996年生，硕士研究生，主要研究领域为文本挖掘；张颖怡，女，1992年生，博士研究生，主要研究领域为自然语言处；

引用本文:

章成志, 胡少虎, 张颖怡. 通用语料的眼动数据对微博关键词抽取的性能提升探究[J]. 情报学报, 2021, 40(4): 375-386.
Zhang Chengzhi, Hu Shaohu, Zhang Yingyi. Investigating the Performance Improvement of Microblog Keyword Extraction by Eye-tracking Data from General-domain Corpus. 情报学报, 2021, 40(4): 375-386.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2021.04.005 或 https://qbxb.istic.ac.cn/CN/Y2021/V40/I4/375

1 Turney P D. Learning algorithms for keyphrase extraction[J]. Information Retrieval, 2000, 2(4): 303-336.
2 Papagiannopoulou E, Tsoumakas G. A review of keyphrase extraction[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2020, 10(2): e1339.
3 Carpenter P A, Just M A. Eye movements in reading[M]. New York: Academic Press, 1983.
4 Zhang Y Y, Zhang C Z. Using human attention to extract keyphrase from microblog post[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2019: 5867-5872.
5 Lau J H, Baldwin T. An empirical evaluation of doc2vec with practical insights into document embedding generation[C]// Proceedings of the 1st Workshop on Representation Learning for NLP. Stroudsburg: Association for Computational Linguistics, 2016: 78-86.
6 Pagliardini M, Gupta P, Jaggi M. Unsupervised learning of sentence embeddings using compositional n-gram features[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018: 528-540.
7 Pennington J, Socher R, Manning C D. GloVe: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543.
8 Witten I H, Paynter G W, Frank E, et al. KEA: practical automatic keyphrase extraction[C]// Proceedings of the 4th ACM Conference on Digital Libraries. New York: ACM Press, 1999: 254-255.
9 Jiang X, Hu Y H, Li H. A ranking approach to keyphrase extraction[C]// Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2009: 756-757.
10 章成志, 苏新宁. 基于条件随机场的自动标引模型研究[J]. 中国图书馆学报, 2008, 34(5): 89-94,99.
11 Gollapalli S D, Li X L, Yang P. Incorporating expert knowledge into keyphrase extraction[C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 3180-3187.
12 Zhang Q, Wang Y, Gong Y Y, et al. Keyphrase extraction using deep recurrent neural networks on twitter[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 836-845.
13 Meng R, Zhao S Q, Han S G, et al. Deep keyphrase generation[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 582-592.
14 Basaldella M, Antolli E, Serra G, et al. Bidirectional LSTM recurrent neural network for keyphrase extraction[C]// Proceedings of the Digital Libraries and Multimedia Archives:14th Italian Research Conference on Digital Libraries. Cham: Springer, 2017: 180-187.
15 Zhang Y Y, Li J, Song Y, et al. Encoding conversation context for neural keyphrase extraction from microblog posts[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018: 1676-1686.
16 Rayner K. Eye movements in reading and information processing: 20 years of research[J]. Psychological Bulletin, 1998, 124(3): 372-422.
17 Kennedy A, Pynte J, Murray W S, et al. Frequency and predictability effects in the Dundee Corpus: an eye movement analysis[J]. Quarterly Journal of Experimental Psychology, 2013, 66(3): 601-618.
18 Kliegl R, Grabner E, Rolfs M, et al. Length, frequency, and predictability effects of words on eye movements in reading[J]. European Journal of Cognitive Psychology, 2004, 16(1/2): 262-284.
19 Luke S G, Christianson K. The Provo Corpus: a large eye-tracking corpus with predictability norms[J]. Behavior Research Methods, 2018, 50(2): 826-833.
20 Cop U, Dirix N, Drieghe D, et al. Presenting GECO: an eyetracking corpus of monolingual and bilingual sentence reading[J]. Behavior Research Methods, 2017, 49(2): 602-615.
21 Hollenstein N, Rotsztejn J, Troendle M, et al. ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading[J]. Scientific Data, 2018, 5: 180291.
22 Barrett M, Bingel J, Keller F, et al. Weakly supervised part-of-speech tagging using eye-tracking data[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 579-584.
23 Li S, Gra?a J V, Taskar B. Wiki-ly supervised part-of-speech tagging[C]// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2012: 1389-1398.
24 Mishra A, Kanojia D, Nagar S, et al. Leveraging cognitive features for sentiment analysis[C]// Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2016: 156-166.
25 Barrett M, S?gaard A. Reading behavior predicts syntactic categories[C]// Proceedings of the 19th Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2015: 345-349.
26 Barrett M, Bingel J, Hollenstein N, et al. Sequence classification with human attention[C]// Proceedings of the 22nd Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2018: 302-312.
27 Zeng X S, Li J, Wang L, et al. Microblog conversation recommendation via joint modeling of topics and discourse[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018: 375-385.
28 Campos R, Mangaravite V, Pasquali A, et al. YAKE! collection-independent automatic keyword extractor[C]// Proceedings of the 40th European Conference on IR Research. Cham: Springer, 2018: 806-810.
29 Chen W, Gao Y F, Zhang J N, et al. Title-guided encoding for keyphrase generation[C]// Proceedings of the 33th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2019: 6268-6275.
30 Jebbara S, Cimiano P. Improving opinion-target extraction with character-level word embeddings[C]// Proceedings of the First Workshop on Subword and Character Level Models in NLP. Stroudsburg: Association for Computational Linguistics, 2017: 159-167.
31 Graves A. Generating sequences with recurrent neural networks[EB/OL]. (2013-08-04). https://arxiv.org/pdf/1308.0850.pdf.