Estela Saquete

Also published as: Estela Saquete Boro, E. Saquete


2019

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Team GPLSI. Approach for automated fact checking
Aimée Alonso-Reina | Robiert Sepúlveda-Torres | Estela Saquete | Manuel Palomar
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Fever Shared 2.0 Task is a challenge meant for developing automated fact checking systems. Our approach for the Fever 2.0 is based on a previous proposal developed by Team Athene UKP TU Darmstadt. Our proposal modifies the sentence retrieval phase, using statement extraction and representation in the form of triplets (subject, object, action). Triplets are extracted from the claim and compare to triplets extracted from Wikipedia articles using semantic similarity. Our results are satisfactory but there is room for improvement.

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SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions
Miguel A. García-Cumbreras | Salud María Jiménez-Zafra | Arturo Montejo-Ráez | Manuel Carlos Díaz-Galiano | Estela Saquete
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.

2015

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GPLSIUA: Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering
Borja Navarro | Estela Saquete
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2012

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TIMEN: An Open Temporal Expression Normalisation Resource
Hector Llorens | Leon Derczynski | Robert Gaizauskas | Estela Saquete
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Temporal expressions are words or phrases that describe a point, duration or recurrence in time. Automatically annotating these expressions is a research goal of increasing interest. Recognising them can be achieved with minimally supervised machine learning, but interpreting them accurately (normalisation) is a complex task requiring human knowledge. In this paper, we present TIMEN, a community-driven tool for temporal expression normalisation. TIMEN is derived from current best approaches and is an independent tool, enabling easy integration in existing systems. We argue that temporal expression normalisation can only be effectively performed with a large knowledge base and set of rules. Our solution is a framework and system with which to capture this knowledge for different languages. Using both existing and newly-annotated data, we present results showing competitive performance and invite the IE community to contribute to a knowledge base in order to solve the temporal expression normalisation problem.

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Massively Increasing TIMEX3 Resources: A Transduction Approach
Leon Derczynski | Héctor Llorens | Estela Saquete
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. Gold standard temporally-annotated resources are limited in size, which makes research using them difficult. Standards have also evolved over the past decade, so not all temporally annotated data is in the same format. We vastly increase available human-annotated temporal expression resources by converting older format resources to TimeML/TIMEX3. This task is difficult due to differing annotation methods. We present a robust conversion tool and a new, large temporal expression resource. Using this, we evaluate our conversion process by using it as training data for an existing TimeML annotation tool, achieving a 0.87 F1 measure - better than any system in the TempEval-2 timex recognition exercise.

2011

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Data-Driven Approach Using Semantics for Recognizing and Classifying TimeML Events in Italian
Tommaso Caselli | Hector Llorens | Borja Navarro-Colorado | Estela Saquete
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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TimeML Events Recognition and Classification: Learning CRF Models with Semantic Roles
Hector Llorens | Estela Saquete | Borja Navarro-Colorado
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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TIPSem (English and Spanish): Evaluating CRFs and Semantic Roles in TempEval-2
Hector Llorens | Estela Saquete | Borja Navarro
Proceedings of the 5th International Workshop on Semantic Evaluation

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ID 392:TERSEO + T2T3 Transducer. A systems for Recognizing and Normalizing TIMEX3
Estela Saquete Boro
Proceedings of the 5th International Workshop on Semantic Evaluation

2009

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Using Semantic Networks to Identify Temporal Expressions from Semantic Roles
Hector Llorens | Borja Navarro | Estela Saquete
Proceedings of the International Conference RANLP-2009

2006

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Evaluating Knowledge-based Approaches to the Multilingual Extension of a Temporal Expression Normalizer
Matteo Negri | Estela Saquete | Patricio Martínez-Barco | Rafael Muñoz
Proceedings of the Workshop on Annotating and Reasoning about Time and Events

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Multilingual Extension of a Temporal Expression Normalizer using Annotated Corpora
E. Saquete | P. Martínez-Barco | R. Muñoz | M. Negri | M. Speranza | R. Sprugnoli
Proceedings of the Cross-Language Knowledge Induction Workshop

2004

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Splitting Complex Temporal Questions for Question Answering Systems
E. Saquete | P. Martínez-Barco | R. Muñoz | J.L. Vicedo
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)