Hector Llorens

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Within the SemEval-2013 evaluation exercise, the TempEval-3 shared task aims to advance research on temporal information processing. It follows on from TempEval-1 and -2, with: a three-part structure covering temporal expression, event, and temporal relation extraction; a larger dataset; and new single measures to rank systems – in each task and in general.(More)
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. Although the used features include different language analysis levels, the approach is focused on semantic information. For Spanish, TIPSem achieved the best F1 score in all the tasks. For(More)
In this proposal, we describe the forthcoming TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal information processing. TempEval-3 follows on from previous TempEval events, incorporating: a three-part task structure covering event, temporal expression and(More)
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 supervised machine learning, but interpreting them accurately (normalisation) is a complex task requiring human knowledge. In this paper,(More)
This paper presents a multilayered architecture that enhances the capabilities of current QA systems and allows different types of complex questions or queries to be processed. The answers to these questions need to be gathered from factual information scattered throughout different documents. Specifically, we designed a specialized layer to process the(More)
This paper analyzes the contribution of semantic roles to TimeML event recognition and classification. For that purpose, an approach using conditional random fields with a variety of morphosyntactic features plus semantic roles features is developed and evaluated. Our system achieves an F1 of 81.4% in recognition and a 64.2% in classification. We(More)
QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information relevant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation(More)
In corpus linguistics obtaining high-quality semantically-annotated corpora is a fundamental goal. Various annotations of the same text can be obtained from automated systems, human annotators, or a combination of both. Obtaining, by manual means, a merged annotation from these, which improves the correctness of each individual annotation, is costly. We(More)
The temporal annotation scheme Time ML was developed to support research in complex temporal question answering (QA). Given the complexity of temporal QA, most of the efforts have focused, so far, on extracting temporal information, which has been evaluated with corpus-based evaluation. However, the QA task represents a natural way to evaluate temporal(More)
Strains of Vibrio vulnificus biotype 2, isolated from internal organs of diseased European eels as pure cultures of opaque cells, together with some reference strains from Japanese eels, were used in this study. Spontaneous translucent-phase variants were obtained from the corresponding parent strains and compared for a variety of phenotypic traits related(More)