Search Results for author: Mikel Iruskieta

Found 14 papers, 0 papers with code

The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification

no code implementations EMNLP (DISRPT) 2021 Amir Zeldes, Yang Janet Liu, Mikel Iruskieta, Philippe Muller, Chloé Braud, Sonia Badene

In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking).

Connective Detection Relation +1

The DISRPT 2019 Shared Task on Elementary Discourse Unit Segmentation and Connective Detection

no code implementations WS 2019 Amir Zeldes, Debopam Das, Erick Galani Maziero, Juliano Antonio, Mikel Iruskieta

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.

Connective Detection

EusDisParser: improving an under-resourced discourse parser with cross-lingual data

no code implementations WS 2019 Mikel Iruskieta, Chlo{\'e} Braud

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.

Multilingual Word Embeddings

Towards discourse annotation and sentiment analysis of the Basque Opinion Corpus

no code implementations WS 2019 Jon Alkorta, Koldo Gojenola, Mikel Iruskieta

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.

Sentiment Analysis

The RST Spanish-Chinese Treebank

no code implementations COLING 2018 Shuyuan Cao, Iria da Cunha, Mikel Iruskieta

Discourse analysis is necessary for different tasks of Natural Language Processing (NLP).

A Corpus-based Approach for Spanish-Chinese Language Learning

no code implementations WS 2016 Shuyuan Cao, Iria da Cunha, Mikel Iruskieta

Due to the huge population that speaks Spanish and Chinese, these languages occupy an important position in the language learning studies.

Discourse Segmentation POS +1

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