Jorge Carrillo de Albornoz

Also published as: Jorge Carrillo de Albornoz, Jorge Carrillo-de-Albornoz


2020

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An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results
Enrique Amigo | Julio Gonzalo | Stefano Mizzaro | Jorge Carrillo-de-Albornoz
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as “positive”, “neutral”, “negative” in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.

2012

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SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis
Jorge Carrillo de Albornoz | Laura Plaza | Pablo Gervás
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word ambiguity problem using one of the many WordNet-based word sense disambiguation algorithms. SentiSense consists of 5,496 words and 2,190 synsets labeled with an emotion from a set of 14 emotional categories, which are related by an antonym relationship. SentiSense has been developed semi-automatically using several semantic relations between synsets in WordNet. SentiSense is endowed with a set of tools that allow users to visualize the lexicon and some statistics about the distribution of synsets and emotions in SentiSense, as well as to easily expand the lexicon. SentiSense is available for research purposes.

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UCM-I: A Rule-based Syntactic Approach for Resolving the Scope of Negation
Jorge Carrillo de Albornoz | Laura Plaza | Alberto Díaz | Miguel Ballesteros
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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UCM-2: a Rule-Based Approach to Infer the Scope of Negation via Dependency Parsing
Miguel Ballesteros | Alberto Díaz | Virginia Francisco | Pablo Gervás | Jorge Carrillo de Albornoz | Laura Plaza
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2010

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A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification
Jorge Carrillo de Albornoz | Laura Plaza | Pablo Gervás
Proceedings of the Fourteenth Conference on Computational Natural Language Learning