Luis Espinosa Anke

Supervised Distributional Hypernym Discovery via Domain Adaptation

Comunicacio presentada a la Conference on Empirical Methods in Natural Language Processing celebrada els dies 1 a 5 de novembre de 2016 a Austin, Texas.

Knowledge Base Unification via Sense Embeddings and Disambiguation

We present KB-UNIFY, a novel approach for integrating the output of different Open Information Extraction systems into a single unified and fully disambiguated knowledge repository. KB-UNIFY consists of three main steps: (1) disambiguation of relation argument pairs via a sensebased vector representation and a large unified sense inventory; (2) ranking of semantic relations according to their degree of specificity; (3) cross-resource relation alignment and merging based on the semantic similarity of domains and ranges. We tested KB-UNIFY on a set of four heterogeneous knowledge bases, obtaining high-quality results. We discuss and provide evaluations at each stage, and release output and evaluation data for the use and scrutiny of the community 1 .

Semantics-driven recognition of collocations using word embeddings

L2 learners often produce “ungrammatical” word combinations such as, e.g., *give a suggestion or *make a walk. This is because of the “collocationality” of one of their items (the base) that limits the acceptance of collocates to express a specific meaning (‘perform’ above). We propose an algorithm that delivers, for a given base and the intended meaning of a collocate, the actual collocate lexeme(s) (make / take above). The algorithm exploits the linear mapping between bases and collocates from examples and generates a collocation transformation matrix which is then applied to novel unseen cases. The evaluation shows a promising line of research in collocation discovery.

TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features.

Comunicacio presentada al 10th International Workshop on Semantic Evaluation (SemEval 2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, EUA.

TALN-UPF: Taxonomy Learning Exploiting CRF-Based Hypernym Extraction on Encyclopedic Definitions

This paper describes the system submitted by the TALN-UPF team to SEMEVAL Task 17 (Taxonomy Extraction Evaluation). We present a method for automatically learning a taxonomy from a flat terminology, which benefits from a definition corpus obtained by querying the BabelNet semantic network. Then, we combine a machine-learning algorithm for term-hypernym extraction with linguistically-motivated heuristics for hypernym decomposition. Our approach performs well in terms of vertex coverage and newly added vertices, while it shows room for improvement in terms of graph topology, edge coverage and precision of novel edges.

Savana: Re-using Electronic Health Records with Artificial Intelligence

Health information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing “mindlines” against “guidelines”, these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management.

TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings.

This paper describes the participation of the TALN team in SemEval-2016 Task 14: Semantic Taxonomy Enrichment. The purpose of the task is to find the best point of attachment in WordNet for a set of Out of Vocabulary (OOV) terms. These may come, to name a few, from domain specific glossaries, slang or typical jargon from Internet forums and chatrooms. Our contribution takes as input an OOV term, its part of speech and its associated definition, and generates a set of WordNet synset candidates derived from modelling the term’s definition as a sense embedding representation. We leverage a BabelNet-based vector space representation, which allows us to map the algorithm’s prediction to WordNet. Our approach is designed to be generic and fitting to any domain, without exploiting, for instance, HTML markup in source web pages. Our system performs above the median of all submitted systems, and rivals in performance a powerful baseline based on extracting the first word of the definition with the same partof-speech as the OOV term.