Examinando por Autor "Hovy, Eduard H."

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  • 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
    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.