Annotating Uncertainty in Hungarian Webtext

Abstract

Uncertainty detection has been a popular topic in natural language processing, which manifested in the creation of several corpora for English. Here we show how the annotation guidelines originally developed for English standard texts can be adapted to Hungarian webtext. We annotated a small corpus of Facebook posts for uncertainty phenomena and we illustrate the main characteristics of such texts, with special regard to uncertainty annotation. Our results may be exploited in adapting the guidelines to other languages or domains and later on, in the construction of automatic uncertainty detectors. BackgroundDetecting uncertainty in natural language texts has received a considerable amount of attention in the last decade (Farkas et al., 2010;Morante and Sporleder, 2012). Several manually annotated corpora have been created, which serve as training and test databases of state-of-the-art uncertainty detectors based on supervised machine learning techniques. Most of these corpora are constructed for English, however, their domains and genres are diverse: biological texts (Medlock and Briscoe, 2007;Kim et al., 2008;Settles et al., 2008;Shatkay et al., 2008;Vincze et al., 2008;Nawaz et al., 2010) The diversity of the resources also manifests in the fact that the annotation principles behind the corpora might slightly differ, which led Szarvas et al. (2012) to compare the annotation schemes of three corpora (BioScope, FactBank and WikiWeasel) and they offered a unified classification of semantic uncertainty phenomena, on the basis of which these corpora were reannotated, using uniform guidelines. Some other uncertainty-related linguistic phenomena are described as discourse-level uncertainty in Vincze (2013). As a first objective of our paper, we will carry out a pilot study and investigate how these unified guidelines can be adapted to texts written in a language that is typologically different from English, namely, Hungarian.As a second goal, we will also focus on annotating texts in a new domain: social media textsapart from Wei et al. (2013) -have not been extensively investigated from the uncertainty detection perspective. As the use and communication through the internet is becoming more and more important in people's lives, the huge amount of data available from this domain is a valuable source of information for computation linguistics. However, processing texts from the web -especially social media texts from blogs, status updates, chat logs and comments -revealed that they are very challenging for applications trained on standard texts. Most studies in this area focus on English, for instance, sentiment analysis from tweets has been the focus of recent challenges (Wilson et al., 2013) and Facebook posts have been analysed from the perspective of computational psychology (Celli et al., 2013). A syntactically