Papers by Maite Martin
TANDO: A Corpus for Document-level Machine Translation (2022.lrec-1)
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Harritxu Gete, Thierry Etchegoyhen, David Ponce, Gorka Labaka, Nora Aranberri, Ander Corral, Xabier Saralegi, Igor Ellakuria, Maite Martin
| Challenge: | Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information. |
| Approach: | They propose to use document-level corpus for Basque-Spanish language pairs to take into account contextual information and perform fine-grained evaluations of gender and gender. |
| Outcome: | The proposed corpus is suitable for fine-grained evaluation of document-level machine translation systems. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
A review of Spanish corpora annotated with negation (C18-1)
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| Challenge: | Existing corpora annotated with negation information are small and not always compatible . negation is a linguistic phenomenon that is not addressed in English . |
| Approach: | They review existing corpora annotated with negation in Spanish and analyze compatibility . they propose to develop a supervised negation processing system for Spanish . |
| Outcome: | The proposed system will not be able to merge the small corpora in Spanish due to lack of compatibility in annotations. |
Using Snomed to recognize and index chemical and drug mentions. (D19-57)
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| Challenge: | a new named entity extraction system is proposed for biological texts . the system is based on machine learning and deep learning . |
| Approach: | They propose a named entity extraction system based on machine learning and deep learning . they propose to map drug names in Spanish biomedical texts using Snomed . |
| Outcome: | The proposed system achieves 78% in the first sub-track and 72% in the second task. |