Eduard H. Hovy

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Following the recent adoption by the machine translation community of automatic evaluation using the BLEU/NIST scoring process, we conduct an in-depth study of a similar idea for evaluating summaries. The results show that automatic evaluation using unigram cooccurrences between summary pairs correlates surprising well with human evaluations, based on(More)
OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of(More)
In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to(More)
Identifying sentiments (the affective parts of opinions) is a challenging problem. We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion. The system contains a module for determining word sentiment and another for combining sentiments within a sentence. We experiment with(More)
In order to produce a good summary, one has to identify the most relevant portions of a given text. We describe in this paper a method for automatically training topic signatures{sets of related words, with associated weights, organized around head topics{and illustrate with signatures we created with 6,194 TREC collection texts over 4 selected topics. We(More)
The MIT Encyclopedia of the Cognitive Sciences (MITECS) brings together 471 brief articles on a very wide range of topics within cognitive science. The general editors worked with advisory editors in six contributing fields, including Gennaro Chierchia on Linguistics and Language and Michael I. Jordan and Stuart Russell on Computational Intelligence. MITECS(More)
We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the(More)
We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important(More)