Hector Llorens

Hector Llorens
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Main area of research
Natural language & AI

Hector Llorens is Senior Research Scientist on the Natural Language Understanding team. He joined Nuance in July 2012. His research interest is in the area of Natural Language & AI. Before joining Nuance, Hector worked for 5 years as a researcher at the University of Alicante (Spain), where he obtained his PhD (2011) in Computer Science. His research focused on natural language processing. In particular, his dissertation presented an analysis of using semantics for multilingual temporal information understanding. This involved the R&D of TIPSem,  a data-driven system to extract and classify temporal expressions, events and their relations for English, Spanish, Italian and Chinese. During that period, he did two research stays: (i) in 2011 at University of Rochester (NY, USA) to research on temporal reasoning and question answering, and (ii) in 2010, at the University of Sheffield (UK) for researching on temporal information visualization.

Selected articles

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial

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Tempeval-3: Evaluating events, time expressions, and temporal relations

We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal

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TIMEN: an open temporal expression normalisation resource

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

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Time-Surfer: time-based graphical access to document content

Abstract. This demonstration presents a novel interactive graphical interface to document content focusing on the time dimension. The objective of Time-Surfer is to let users

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TIPSem (English and Spanish): evaluating CRFs and semantic roles in tempeval-2

This paper presents TIPSem, a system to extract temporal information from natural language texts for English and Spanish. TIPSem, learns CRF models from training data.

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