Abhijeet Gupta


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Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases
V Thejas | Abhijeet Gupta | Sebastian Padó
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Collaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the task of predicting new attributes of entities given partial descriptions of these entities. Virtually all of these models either concentrate on numeric attributes (<Italy,GDP,2T$>) or they concentrate on categorical attributes (<Tim Cook,chairman,Apple>). In this paper, we propose a simple feed-forward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question. Following insights from multi-task learning, our hypothesis is that due to the correlations among attributes of different kinds, joint prediction improves over separate prediction. Our experiments on seven FreeBase domains show that this hypothesis is true of the two attribute types: we find substantial improvements for numeric attributes in the joint model, while performance remains largely unchanged for categorical attributes. Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful ‘background knowledge’ for numeric prediction, while this is true to a lesser degree in the inverse direction.


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Instances and concepts in distributional space
Gemma Boleda | Abhijeet Gupta | Sebastian Padó
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Instances (“Mozart”) are ontologically distinct from concepts or classes (“composer”). Natural language encompasses both, but instances have received comparatively little attention in distributional semantics. Our results show that instances and concepts differ in their distributional properties. We also establish that instantiation detection (“Mozart – composer”) is generally easier than hypernymy detection (“chemist – scientist”), and that results on the influence of input representation do not transfer from hyponymy to instantiation.

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Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible
Abhijeet Gupta | Gemma Boleda | Sebastian Padó
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Word embeddings are supposed to provide easy access to semantic relations such as “male of” (man–woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.


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Distributional vectors encode referential attributes
Abhijeet Gupta | Gemma Boleda | Marco Baroni | Sebastian Padó
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Dissecting the Practical Lexical Function Model for Compositional Distributional Semantics
Abhijeet Gupta | Jason Utt | Sebastian Padó
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics