Mikel Iruskieta

Also published as: M. Iruskieta


2019

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Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
Amir Zeldes | Debopam Das | Erick Maziero Galani | Juliano Desiderato Antonio | Mikel Iruskieta
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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Introduction to Discourse Relation Parsing and Treebanking (DISRPT): 7th Workshop on Rhetorical Structure Theory and Related Formalisms
Amir Zeldes | Debopam Das | Erick Galani Maziero | Juliano Antonio | Mikel Iruskieta
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop’s aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.

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EusDisParser: improving an under-resourced discourse parser with cross-lingual data
Mikel Iruskieta | Chloé Braud
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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 demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.

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The DISRPT 2019 Shared Task on Elementary Discourse Unit Segmentation and Connective Detection
Amir Zeldes | Debopam Das | Erick Galani Maziero | Juliano Antonio | Mikel Iruskieta
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.

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Multilingual segmentation based on neural networks and pre-trained word embeddings
Mikel Iruskieta | Kepa Bengoetxea | Aitziber Atutxa Salazar | Arantza Diaz de Ilarraza
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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. We report F1 results for 6 languages: Basque (0.853), English (0.919), French (0.907), German (0.913), Portuguese (0.926) and Spanish (0.868 and 0.769). Finally, we also pursued an error analysis based on clause typology for Basque and Spanish, in order to understand the performance of the segmenter.

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Towards discourse annotation and sentiment analysis of the Basque Opinion Corpus
Jon Alkorta | Koldo Gojenola | Mikel Iruskieta
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

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 span by means of coherence relations. This work presents the first results on the annotation of the Basque Opinion Corpus using Rhetorical Structure Theory (RST). Our evaluation results and analysis show us the main avenues to improve on a future annotation process. We have also extracted the subjectivity of several rhetorical relations and the results show the effect of sentiment words in relations and the influence of each relation in the semantic orientation value.

2018

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The RST Spanish-Chinese Treebank
Shuyuan Cao | Iria da Cunha | Mikel Iruskieta
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

Discourse analysis is necessary for different tasks of Natural Language Processing (NLP). As two of the most spoken languages in the world, discourse analysis between Spanish and Chinese is important for NLP research. This paper aims to present the first open Spanish-Chinese parallel corpus annotated with discourse information, whose theoretical framework is based on the Rhetorical Structure Theory (RST). We have evaluated and harmonized each annotation part to obtain a high annotated-quality corpus. The corpus is already available to the public.

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Saying no but meaning yes: negation and sentiment analysis in Basque
Jon Alkorta | Koldo Gojenola | Mikel Iruskieta
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this work, we have analyzed the effects of negation on the semantic orientation in Basque. The analysis shows that negation markers can strengthen, weaken or have no effect on sentiment orientation of a word or a group of words. Using the Constraint Grammar formalism, we have designed and evaluated a set of linguistic rules to formalize these three phenomena. The results show that two phenomena, strengthening and no change, have been identified accurately and the third one, weakening, with acceptable results.

2017

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Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms
M. Taboada | I. da Cunha | E.G. Maziero | P. Cardoso | J.D. Antonio | M. Iruskieta
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

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Deliberation as Genre: Mapping Argumentation through Relational Discourse Structure
Oier Imaz | Mikel Iruskieta
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

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Using lexical level information in discourse structures for Basque sentiment analysis
Jon Alkorta | Koldo Gojenola | Mikel Iruskieta | Maite Taboada
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

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Framework for the Analysis of Simplified Texts Taking Discourse into Account: the Basque Causal Relations as Case Study
Itziar Gonzalez-Dios | Arantza Diaz de Ilarraza | Mikel Iruskieta
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

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Discourse Segmentation for Building a RST Chinese Treebank
Shuyuan Cao | Nianwen Xue | Iria da Cunha | Mikel Iruskieta | Chuan Wang
Proceedings of the 6th Workshop on Recent Advances in RST and Related Formalisms

2016

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A Corpus-based Approach for Spanish-Chinese Language Learning
Shuyuan Cao | Iria da Cunha | Mikel Iruskieta
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

Due to the huge population that speaks Spanish and Chinese, these languages occupy an important position in the language learning studies. Although there are some automatic translation systems that benefit the learning of both languages, there is enough space to create resources in order to help language learners. As a quick and effective resource that can give large amount language information, corpus-based learning is becoming more and more popular. In this paper we enrich a Spanish-Chinese parallel corpus automatically with part of-speech (POS) information and manually with discourse segmentation (following the Rhetorical Structure Theory (RST) (Mann and Thompson, 1988)). Two search tools allow the Spanish-Chinese language learners to carry out different queries based on tokens and lemmas. The parallel corpus and the research tools are available to the academic community. We propose some examples to illustrate how learners can use the corpus to learn Spanish and Chinese.

2014

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The annotation of the Central Unit in Rhetorical Structure Trees: A Key Step in Annotating Rhetorical Relations
Mikel Iruskieta | Arantza Díaz de Ilarraza | Mikel Lersundi
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers