Virginia Francisco

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The adequate representation of emotions in affective computing is an important problem and the starting point of studies related to emotions. There are different approaches for representing emotions,… (More)

Inferring the Scope of Negation in Biomedical Documents

Miguel Ballesteros, Virginia Francisco, Alberto Espuny Díaz, Jesús Herrera, Pablo Gervás
CICLing
2012

In the last few years negation detection systems for biomedical texts have been developed successfully. In this paper we present a system that finds and annotates the scope of negation in English… (More)

EmoTales: creating a corpus of folk tales with emotional annotations

Virginia Francisco, Raquel Hervás, Federico Peinado, Pablo Gervás
Language Resources and Evaluation
2012

Emotions are inherent to any human activity, including human– computer interactions, and that is the reason why recognizing emotions expressed in natural language is becoming a key feature for the… (More)

Exploring the Compositionality of Emotions in Text: Word Emotions, Sentence Emotions and Automated Tagging

Virginia Francisco, Pablo Gervás
2006

This paper presents an approach to automated marking up of texts with emotional labels. The approach considers the representation of emotions as emotional dimensions. A corpus of example texts… (More)

Automatic Generation of Emotionally-Targeted Soundtracks

Kristine Monteith, Virginia Francisco, Tony R. Martinez, Pablo Gervás, Dan Ventura
ICCC
2011

Music can be used both to direct and enhance the impact a story can have on its listeners. This work makes use of two creative systems to provide emotionally-targeted musical accompaniment for… (More)

UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing

Miguel Ballesteros, Alberto Espuny Díaz, Virginia Francisco, Pablo Gervás, Jorge Carrillo de Albornoz, Laura Plaza
*SEM@NAACL-HLT
2012

UCM-2 infers the words that are affected by negations by browsing dependency syntactic structures. It first makes use of an algorithm that detects negation cues, like no, not or nothing, and the… (More)

Improving Parsing Accuracy for Spanish using Maltparser

Miguel Ballesteros, Jesús Herrera, Virginia Francisco, Pablo Gervás
Procesamiento del Lenguaje Natural
2010

In the last years, dependency parsing has been accomplished by machine learning–based systems showing great accuracy but usually under 90% for Labelled Attachment Score (LAS). Maltparser is one of… (More)

Influence of personal choices on lexical variability in referring expressions

Raquel Hervás, Javier Arroyo, Virginia Francisco, Federico Peinado, Pablo Gervás
Natural Language Engineering
2016

A Feasibility Study on Low Level Techniques for Improving Parsing Accuracy for Spanish Using Maltparser

Miguel Ballesteros, Jesús Herrera, Virginia Francisco, Pablo Gervás
SETN
2010

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