Corpora Annotated with Negation: An Overview

Corpora Annotated with Negation: An Overview Salud Mar´ıa Jim´enez-Zafra SINAI, Computer Science Department CEATIC, Universidad de Ja´en sjzafra@ujaen.es Roser Morante CLTL Lab, Computational Linguistics VU University Amsterdam r.morantevallejo@vu.nl Mar´ıa Teresa Mart´ın-Valdivia SINAI, Computer Science Department CEATIC, Universidad de Ja´en maite@ujaen.es L. Alfonso Ure˜na-L´opez SINAI, Computer Science Department CEATIC, Universidad de Ja´en laurena@ujaen.es Negation is a universal linguistic phenomenon with a great qualitative impact on natural lan- guage processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment. Submission received: 4 December 2018; revised version received: 23 October 2019; accepted for publication: 17 November 2019. https://doi.org/10.1162/COLI a 00371 © 2020 Association for Computational Linguistics Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license Computational Linguistics Volume 46, Number 1 1. Introduction Negation is a key universal phenomenon in language. All languages possess differ- ent types of resources (morphological, lexical, syntactic) that allow speakers to speak about properties that people or things do not hold or events that do not happen. The presence of a negation in a sentence can have enormous consequences in many real world situations: A world in which Donald Trump was elected as president would be very different from a world in which Donald Trump was not elected as president, for example. Thus, the presence of a single particle modifying a proposition describes a completely different situation. Negation is a main linguistic phenomenon and the issue of its computational treatment has not been resolved yet due to its complexity, the multiple linguistic forms in which it can appear, and the different ways it can act on the words within its scope. If we want to develop systems that approach human understanding, it is necessary to incorporate the treatment of one of the main linguistic phenomena used by people in their daily communication. Natural language processing (NLP) is a subfield of artificial intelligence that focuses on the processing and generation of human language in order for computers to learn, understand, and produce human language (Hirschberg and Manning 2015). Some lin- guistic phenomena such as negation, speculation, irony, or sarcasm pose challenges for computational natural language learning. One might think that, given the fact that …

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