Persona: Peñas Padilla, Anselmo
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Peñas Padilla
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Anselmo
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Publicación A simple measure to assess non-response(2011-06-19) Peñas Padilla, Anselmo; Rodrigo Yuste, ÁlvaroThere 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.Publicación Temporally anchored relation extraction(2012-12-08) Garrido, Guillermo; Cabaleiro, Bernardo; Peñas Padilla, Anselmo; Rodrigo Yuste, ÁlvaroAlthough 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.Publicación Filling knowledge gaps in text for machine reading(2010-08-22) Hovy, Eduard H.; Peñas Padilla, AnselmoTexts 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.Publicación Study of a Lifelong Learning Scenario for Question Answering(Elsevier, 2022-12-15) Echegoyen, Guillermo; Rodrigo Yuste, Álvaro; Peñas Padilla, AnselmoQuestion Answering (QA) systems have witnessed a significant advance in the last years due to the development of neural architectures employing pre-trained large models like BERT. However, once the QA model is fine-tuned for a task (e.g a particular type of questions over a particular domain), system performance drops when new tasks are added along time, (e.g new types of questions or new domains). Therefore, the system requires a retraining but, since the data distribution has shifted away from the previous learning, performance over previous tasks drops significantly. Hence, we need strategies to make our systems resistant to the passage of time. Lifelong Learning (LL) aims to study how systems can take advantage of the previous learning and the knowledge acquired to maintain or improve performance over time. In this article, we explore a scenario where the same LL based QA system suffers along time several shifts in the data distribution, represented as the addition of new different QA datasets. In this setup, the following research questions arise: (i) How LL based QA systems can benefit from previously learned tasks? (ii) Is there any strategy general enough to maintain or improve the performance over time when new tasks are added? and finally, (iii) How to detect a lack of knowledge that impedes the answering of questions and must trigger a new learning process? To answer these questions, we systematically try all possible training sequencesover three well known QA datasets. Our results show how the learning of a new dataset is sensitive to previous training sequences and that we can find a strategy general enough to avoid the combinatorial explosion of testing all possible training sequences. Thus, when a new dataset is added to the system, the best way to retrain the system without dropping performance over the previous datasets is to randomly merge the new training material with the previous one.Publicación WHAD : Wikipedia historical attributes data. Historical structured data extraction and vandalism detection from the Wikipedia edit history(Springer Verlag (Germany), 2013-05-28) Alfonseca, Enrique; Garrido, Guillermo; Delort, Jean Yves; Peñas Padilla, Anselmo::virtual::5662::600; Peñas Padilla, Anselmo; Peñas Padilla, Anselmo; Peñas Padilla, AnselmoThis paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study.Publicación Evaluating Multilingual Question Answering Systems at CLEF(2010-05-17) Forner, Pamela; Giampiccolo, Danilo; Magnini, Bernardo; Sutcliffe, Richard; Peñas Padilla, Anselmo; Rodrigo Yuste, ÁlvaroThe 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.Publicación Unsupervised discovery of domain-specific knowledge from text(2011-06-19) Hovy, Dirk; Zhang, Chunliang; Hovy, Eduard H.; Peñas Padilla, AnselmoLearning 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.