Coreference Resolution for the Basque Language with BART

Soraluze, Ander; Arregi, Olatz; Arregi, Xabier; Ilarraza, Arantza Díaz de; Kabadjov, Mijail; Poesio, Massimo

doi:10.18653/v1/w16-0710

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Moreover, to contextualize the results we obtained, in Table 3, we can see the results of the neural cross-lingual system in comparison with previous coreference resolution systems for Basque. The results obtained are lower than those obtained by previous rule-based (Soraluze et al, 2015) and ML-based (Soraluze et al, 2016) In Table 5 we can see an example of the type of mistakes in the output of our cross-lingual system. Key refers to gold annotation and response to the output of the system.…”

Section: Resultscontrasting

confidence: 62%

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Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque

Urbizu¹,

Soraluze²,

Arregi³

2019

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

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In this paper, we present a cross-lingual neural coreference resolution system for a lessresourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.

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Section: Resultscontrasting

confidence: 62%

“…As regards the Basque language, this is the first work about neural coreference resolution. Nevertheless, a rule-based coreference resolution system (Soraluze et al, 2015) and a machine learning based system (Soraluze et al, 2016) have been developed. Both of which used a rule-based mention detector (Soraluze et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque

Urbizu¹,

Soraluze²,

Arregi³

2019

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

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“…There is a large existing body of work on coreference resolution spanning from the 1970s of which Poesio et al (2016) provides an exhaustive review. In recent years, the field has been dominated by machine learning approaches -with the notable exception of the rule-based system of H. Wiseman et al 2016), gradually reducing their dependency on rich features coming from preprocessing pipelines using linguistic knowledge.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

2019

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In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name. We evaluate state-of-theart coreference resolution systems for the task of resolving all mentions to named entities. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name to an entity and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she-the student-no name). We introduce new metrics for evaluating named entity coreference that address these discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. We are, however, able to confirm that the state-of-the art system according to traditional evaluations also performs vastly better than other systems on the named entity coreference task.

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EUSKOR: End-to-end coreference resolution system for Basque

Soraluze

Arregi

et al. 2019

PLoS ONE

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This paper describes the process of adapting the Stanford Coreference resolution module to the Basque language, taking into account the characteristics of the language. The module has been integrated in a linguistic analysis pipeline obtaining an end-to-end coreference resolution system for the Basque language. The adaptation process explained can benefit and facilitate other languages with similar characteristics in the implementation of their coreference resolution systems. During the experimentation phase, we have demonstrated that language-specific features have a noteworthy effect on coreference resolution, obtaining a gain in CoNLL score of 7.07 with respect to the baseline system. We have also analysed the effect that preprocessing has in coreference resolution, comparing the results obtained with automatic mentions versus gold mentions. When gold mentions are provided, the results increase 11.5 points in CoNLL score in comparison with results obtained when automatic mentions are used. The contribution of each sieve is analysed concluding that morphology is essential for agglutinative languages to obtain good performance in coreference resolution. Finally, an error analysis of the coreference resolution system is presented which have revealed our system’s weak points and help to determine the improvements of the system. As a result of the error analysis, we have enriched the Basque coreference resolution adding new two sieves, obtaining an improvement of 0.24 points in CoNLL F1 when automatic mentions are used and of 0.39 points when the gold mentions are provided.

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Coreference Resolution for the Basque Language with BART

Cited by 3 publications

References 10 publications

Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque

Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

EUSKOR: End-to-end coreference resolution system for Basque

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