Persona:
Peñas Padilla, Anselmo

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0000-0002-7867-0149
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Peñas Padilla
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Anselmo
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2010 - 20125
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Mostrando 1 - 5 de 5
  • Miniatura
    Publicación
    Evaluating Multilingual Question Answering Systems at CLEF
    (2010-05-17) Forner, Pamela; Giampiccolo, Danilo; Magnini, Bernardo; Sutcliffe, Richard; Peñas Padilla, Anselmo; Rodrigo Yuste, Álvaro
    The paper offers an overview of the key issues raised during the seven years’ activity of the Multilingual Question Answering Track at the Cross Language Evaluation Forum (CLEF). The general aim of the Multilingual Question Answering Track has been to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages, also drawing attention to a number of challenging issues for research in multilingual QA. The paper gives a brief description of how the task has evolved over the years and of the way in which the data sets have been created, presenting also a brief summary of the different types of questions developed. The document collections adopted in the competitions are sketched as well, and some data about the participation are provided. Moreover, the main evaluation measures used to evaluate system performances are explained and an overall analysis of the results achieved is presented.
  • Miniatura
    Publicación
    Unsupervised discovery of domain-specific knowledge from text
    (2011-06-19) Hovy, Dirk; Zhang, Chunliang; Hovy, Eduard H.; Peñas Padilla, Anselmo
    Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and reason about textual input. This requires knowledge about the domain structure (such as entities, classes, and actions) in order to do inference. We present a method to infer this implicit knowledge from unlabeled text. Unlike previous approaches, we use automatically extracted classes with a probability distribution over entities to allow for context-sensitive labeling. From a corpus of 1.4m sentences, we learn about 250k simple propositions about American football in the form of predicateargument structures like “quarterbacks throw passes to receivers”. Using several statistical measures, we show that our model is able to generalize and explain the data statistically significantly better than various baseline approaches. Human subjects judged up to 96.6% of the resulting propositions to be sensible. The classes and probabilistic model can be used in textual enrichment to improve the performance of LbR end-to-end systems.
  • Miniatura
    Publicación
    Filling knowledge gaps in text for machine reading
    (2010-08-22) Hovy, Eduard H.; Peñas Padilla, Anselmo
    Texts are replete with gaps, information omitted since authors assume a certain amount of background knowledge. We define the process of enrichment that fills these gaps. We describe how enrichment can be performed using a Background Knowledge Base built from a large corpus. We evaluate the effectiveness of various openly available background knowledge bases and we identify the kind of information necessary for enrichment.
  • Miniatura
    Publicación
    A simple measure to assess non-response
    (2011-06-19) Peñas Padilla, Anselmo; Rodrigo Yuste, Álvaro
    There are several tasks where is preferable not responding than responding incorrectly. This idea is not new, but despite several previous attempts there isn’t a commonly accepted measure to assess non-response. We study here an extension of accuracy measure with this feature and a very easy to understand interpretation. The measure proposed (c@1) has a good balance of discrimination power, stability and sensitivity properties. We show also how this measure is able to reward systems that maintain the same number of correct answers and at the same time decrease the number of incorrect ones, by leaving some questions unanswered. This measure is well suited for tasks such as Reading Comprehension tests, where multiple choices per question are given, but only one is correct.
  • Miniatura
    Publicación
    Temporally anchored relation extraction
    (2012-12-08) Garrido, Guillermo; Cabaleiro, Bernardo; Peñas Padilla, Anselmo; Rodrigo Yuste, Álvaro
    Although much work on relation extraction has aimed at obtaining static facts, many of the target relations are actually fluents, as their validity is naturally anchored to a certain time period. This paper proposes a methodological approach to temporally anchored relation extraction. Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. We use a rich graphbased document-level representation to generate novel features for this task. Results show that our implementation for temporal anchoring is able to achieve a 69% of the upper bound performance imposed by the relation extraction step. Compared to the state of the art, the overall system achieves the highest precision reported.