Persona:
Amigo Cabrera, Enrique

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0003-1482-824X
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Amigo Cabrera
Nombre de pila
Enrique
Nombre

Filtros

2020 - 20254
Restablecer filtros

Ajustes

Fecha de inicio: 2020 × Tiene archivos: true ×

Resultados de la búsqueda

Mostrando 1 - 4 de 4
  • Miniatura
    Publicación
    Automatic Generation of Entity-Oriented Summaries for Reputation Management
    (Springer, 2020-04-01) Rodríguez Vidal, Javier; Verdejo, Julia; Carrillo de Albornoz Cuadrado, Jorge Amando; Amigo Cabrera, Enrique; Plaza Morales, Laura; Gonzalo Arroyo, Julio Antonio
    Producing online reputation summaries for an entity (company, brand, etc.) is a focused summarization task with a distinctive feature: issues that may affect the reputation of the entity take priority in the summary. In this paper we (i) present a new test collection of manually created (abstractive and extractive) reputation reports which summarize tweet streams for 31 companies in the banking and automobile domains; (ii) propose a novel methodology to evaluate summaries in the context of online reputation monitoring, which profits from an analogy between reputation reports and the problem of diversity in search; and (iii) provide empirical evidence that producing reputation reports is different from a standard summarization problem, and incorporating priority signals is essential to address the task effectively.
  • Miniatura
    Publicación
    An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results
    (Association for Computational Linguistics Note:, 2020-07-01) Amigo Cabrera, Enrique; Gonzalo Arroyo, Julio Antonio; Mizzarro, Stefano; Carrillo de Albornoz Cuadrado, Jorge Amando
    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.
  • Miniatura
    Publicación
    Information Theory–based Compositional Distributional Semantics
    (Massachusetts Institute of Technology Press, 2022-12-01) Amigo Cabrera, Enrique; Ariza Casabona, Alejandro; Fresno Fernández, Víctor Diego; Martí, M. Antònia; Agencia Estatal de Investigación (España); European Commission
    In the context of text representation, Compositional Distributional Semantics models aim to fuse the Distributional Hypothesis and the Principle of Compositionality. Text embedding is based on co-ocurrence distributions and the representations are in turn combined by compositional functions taking into account the text structure. However, the theoretical basis of compositional functions is still an open issue. In this article we define and study the notion of Information Theory–based Compositional Distributional Semantics (ICDS): (i) We first establish formal properties for embedding, composition, and similarity functions based on Shannon’s Information Theory; (ii) we analyze the existing approaches under this prism, checking whether or not they comply with the established desirable properties; (iii) we propose two parameterizable composition and similarity functions that generalize traditional approaches while fulfilling the formal properties; and finally (iv) we perform an empirical study on several textual similarity datasets that include sentences with a high and low lexical overlap, and on the similarity between words and their description. Our theoretical analysis and empirical results show that fulfilling formal properties affects positively the accuracy of text representation models in terms of correspondence (isometry) between the embedding and meaning spaces.
  • Miniatura
    Publicación
    Evaluating Sequence Labeling on the basis of Information Theory
    (Association for Computational Linguistics, 2025-07-01) Amigo Cabrera, Enrique; Álvarez Mellado, Elena; Carrillo de Albornoz Cuadrado, Jorge Amando; European Commission; Agensia Estatal de Investigación (España)
    Various metrics exist for evaluating sequence labeling problems (strict span matching, token oriented metrics, token concurrence in sequences, etc.), each of them focusing on certain aspects of the task. In this paper, we define a comprehensive set of formal properties that captures the strengths and weaknesses of the existing metric families and prove that none of them is able to satisfy all properties simultaneously. We argue that it is necessary to measure how much information (correct or noisy) each token in the sequence contributes depending on different aspects such as sequence length, number of tokens annotated by the system, token specificity, etc. On this basis, we introduce the Sequence Labelling Information Contrast Model (SL-ICM), a novel metric based on information theory for evaluating sequence labeling tasks. Our formal analysis and experimentation show that the proposed metric satisfies all properties simultaneously.