Ander Soraluze


2020

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Dealing with dialectal variation in the construction of the Basque historical corpus
Ainara Estarrona | Izaskun Etxeberria | Ricardo Etxepare | Manuel Padilla-Moyano | Ander Soraluze
Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects

This paper analyses the challenge of working with dialectal variation when semi-automatically normalising and analysing historical Basque texts. This work is part of a more general ongoing project for the construction of a morphosyntactically annotated historical corpus of Basque called Basque in the Making (BIM): A Historical Look at a European Language Isolate, whose main objective is the systematic and diachronic study of a number of grammatical features. This will be not only the first tagged corpus of historical Basque, but also a means to improve language processing tools by analysing historical Basque varieties more or less distant from present-day standard Basque.

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Sequence to Sequence Coreference Resolution
Gorka Urbizu | Ander Soraluze | Olatz Arregi
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

Until recently, coreference resolution has been a critical task on the pipeline of any NLP task involving deep language understanding, such as machine translation, chatbots, summarization or sentiment analysis. However, nowadays, those end tasks are learned end-to-end by deep neural networks without adding any explicit knowledge about coreference. Thus, coreference resolution is used less in the training of other NLP tasks or trending pretrained language models. In this paper we present a new approach to face coreference resolution as a sequence to sequence task based on the Transformer architecture. This approach is simple and universal, compatible with any language or dataset (regardless of singletons) and easier to integrate with current language models architectures. We test it on the ARRAU corpus, where we get 65.6 F1 CoNLL. We see this approach not as a final goal, but a means to pretrain sequence to sequence language models (T5) on coreference resolution.

2019

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Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque
Gorka Urbizu | Ander Soraluze | Olatz Arregi
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced 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.

2017

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Enriching Basque Coreference Resolution System using Semantic Knowledge sources
Ander Soraluze | Olatz Arregi | Xabier Arregi | Arantza Díaz de Ilarraza
Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

In this paper we present a Basque coreference resolution system enriched with semantic knowledge. An error analysis carried out revealed the deficiencies that the system had in resolving coreference cases in which semantic or world knowledge is needed. We attempt to improve the deficiencies using two semantic knowledge sources, specifically Wikipedia and WordNet.

2016

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Coreference Resolution for the Basque Language with BART
Ander Soraluze | Olatz Arregi | Xabier Arregi | Arantza Díaz de Ilarraza | Mijail Kabadjov | Massimo Poesio
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)

2011

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Recognition and Classification of Numerical Entities in Basque
Ander Soraluze | Iñaki Alegria | Olatz Ansa | Olatz Arregi | Xabier Arregi
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011