Sara Rodríguez-Fernández


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Generation of a Spanish Artificial Collocation Error Corpus
Sara Rodríguez-Fernández | Roberto Carlini | Leo Wanner
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Semantics-Driven Recognition of Collocations Using Word Embeddings
Sara Rodríguez-Fernández | Luis Espinosa-Anke | Roberto Carlini | Leo Wanner
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning
Luis Espinosa-Anke | Jose Camacho-Collados | Sara Rodríguez-Fernández | Horacio Saggion | Leo Wanner
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

WordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending it manually is costly. One important type of relation which could potentially add enormous value to WordNet is the inclusion of collocational information, which is paramount in tasks such as Machine Translation, Natural Language Generation and Second Language Learning. In this paper, we present ColWordNet (CWN), an extended WordNet version with fine-grained collocational information, automatically introduced thanks to a method exploiting linear relations between analogous sense-level embeddings spaces. We perform both intrinsic and extrinsic evaluations, and release CWN for the use and scrutiny of the community.

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Example-based Acquisition of Fine-grained Collocation Resources
Sara Rodríguez-Fernández | Roberto Carlini | Luis Espinosa Anke | Leo Wanner
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Collocations such as “heavy rain” or “make [a] decision”, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate to express a particular meaning, e.g., both “heavy” and “strong” express the meaning ‘intense’, but while “rain” selects “heavy”, “wind” selects “strong”. Lexical Functions (LFs) describe the meanings that hold between the elements of collocations, such as ‘intense’, ‘perform’, ‘create’, ‘increase’, etc. Language resources with semantically classified collocations would be of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised approach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which, given an example of a collocation for each of the target LFs and a set of bases, the system retrieves a list of collocates for each base and LF.


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Classification of Lexical Collocation Errors in the Writings of Learners of Spanish
Sara Rodríguez-Fernández | Roberto Carlini | Leo Wanner
Proceedings of the International Conference Recent Advances in Natural Language Processing