Alberto Garcia-Duran

Also published as: Alberto García-Durán


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Low-Rank Subspaces for Unsupervised Entity Linking
Akhil Arora | Alberto Garcia-Duran | Robert West
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base. Eigenthemes exploits the fact that the entities that are truly mentioned in a document (the “gold entities”) tend to form a semantically dense subset of the set of all candidate entities in the document. Geometrically speaking, when representing entities as vectors via some given embedding, the gold entities tend to lie in a low-rank subspace of the full embedding space. Eigenthemes identifies this subspace using the singular value decomposition and scores candidate entities according to their proximity to the subspace. On the empirical front, we introduce multiple strong baselines that compare favorably to (and sometimes even outperform) the existing state of the art. Extensive experiments on benchmark datasets from a variety of real-world domains showcase the effectiveness of our approach.


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Learning Sequence Encoders for Temporal Knowledge Graph Completion
Alberto García-Durán | Sebastijan Dumančić | Mathias Niepert
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.


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Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban | Alberto García-Durán | Caglar Gulcehre | Sungjin Ahn | Sarath Chandar | Aaron Courville | Yoshua Bengio
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Composing Relationships with Translations
Alberto García-Durán | Antoine Bordes | Nicolas Usunier
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing