Treebanking user-generated content: a UD based overview of guidelines, corpora and unified recommendations

Author:

Sanguinetti ManuelaORCID,Bosco Cristina,Cassidy Lauren,Çetinoğlu Özlem,Cignarella Alessandra Teresa,Lynn Teresa,Rehbein Ines,Ruppenhofer Josef,Seddah Djamé,Zeldes Amir

Abstract

AbstractThis article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Linguistics and Language,Education,Language and Linguistics

Reference89 articles.

1. Albogamy, F., & Ramsay, A. (2017). Universal dependencies for Arabic Tweets. In International conference recent advances in natural language processing, (RANLP) (pp. 46–51).

2. Aufrant, L., Wisniewski, G., & Yvon, F. (2017). LIMSI@CoNLL’17: UD shared task. In Proceedings of the CoNLL 2017 shared task: Multilingual parsing from raw text to universal dependencies (pp. 163–173).

3. Azzi, A. A., Bouamor, H., & Ferradans, S. (2019). The FinSBD-2019 shared task: Sentence boundary detection in PDF noisy text in the financial domain. In Proceedings of the first workshop on financial technology and natural language processing (pp. 74–80), Macao, China. https://www.aclweb.org/anthology/W19-5512

4. Balahur, A. (2013). Sentiment analysis in social media texts. In Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 120–128), Atlanta, GA.

5. Behzad, S., & Zeldes, A. (2020). A cross-genre ensemble approach to robust reddit part of speech tagging. In Proceedings of the 12th web as corpus workshop (WAC-XII) (pp. 50–56), Marseille, France.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3