Paper

Unitate fraseologiko asko idiomatikoki eta literalki uler daitezke. Esate baterako, ziria sartzeak bi esanahi izan ditzake testuinguruaren arabera: norbaiti iruzur egitea edo nonbait ziri bat sartzea literalki. Lan honetan, corpusetan oinarritutako azterketa eleaniztun baten berri emango dugu, eta erakutsiko dugu, batetik, halako hitz-konbinazioak oso gutxitan erabiltzen direla literalki praktikan, eta bestetik, idiomatiko-literal bereizketa

The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments. Elementary Discourse Units (EDUs) are quite similar across different theories. Segmentation is the very first stage on the way of rhetorical annotation. Still, each annotation project adopted several decisions with consequences not only on the annotation of the relational discourse structure but also at the segmentation stage. In this shared task, we have employed pre-trained word embeddings, neural networks (BiLSTM+CRF) to perform the segmentation.

Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first parser based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser.

Discourse information is crucial for a better understanding of the text structure and it is also necessary to describe which part of an opinionated text is more relevant or to decide how a text span can change the polarity (strengthen or weaken) of other spans by means of coherence relations. This work presents the first results on the annotation of the Basque Opinion Corpus using Rhetorical Structure Theory (RST).

This paper presents a study about methods for normalization of historical texts. The aim of these methods is learning relations between historical and contemporary word forms. We have compiled training and test corpora for different languages and scenarios, and we have tried to read the results related to the features of the corpora and languages. Our proposed method, based on weighted finite-state transducers, is com- pared to previously published ones. Our method learns to map phonological changes using a noisy channel