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This paper describes an overview of RITE-2 (Recognizing Inference in TExt) task in NTCIR-10. We evaluated systems that automatically recognize semantic relations between sentences such as paraphrase, entailment, contradiction in Japanese, Simplified Chinese and Traditional Chi-nese. The tasks in RITE-2 are Binary Classification of entailment (BC Subtask),(More)
In this paper, we investigate the answer type detection methods for realizing the Universal Question Answering (UQA), which returns an answer for any given question. For this purpose, the questions collected from a WWW question portal community site were analyzed to see how many kinds of questions were submitted in the real world. Then, we introduce the(More)
On the Internet, users often encounter noise in the form of spelling errors or unknown words, however, dishonest, unreliable, or biased information also acts as noise that makes it difficult to find credible sources of information. As people come to rely on the Internet for more and more information, reducing this credibility noise grows ever more urgent.(More)
This paper presents novel methods for modeling numerical common sense: the ability to infer whether a given number (e.g., three billion) is large, small, or normal for a given context (e.g., number of people facing a water shortage). We first discuss the necessity of numerical common sense in solving textual entailment problems. We explore two approaches(More)
Recognizing semantic relations between sentences, such as entailment and contradiction, is a challenging task that requires detailed analysis of the interaction between diverse linguistic phenomena. In this paper, we propose a latent discriminative model that unifies a statistical framework and a theory of Natural Logic to capture complex interactions(More)
Given podcasts (audio blogs) that are sets of speech files called episodes, this paper describes a method for retrieving episodes that have similar content. Although most previous retrieval methods were based on bibliographic information, tags, or users' playback behaviors without considering spoken content , our method can compute content-based similarity(More)
Since the problem of textual entailment recognition requires capturing semantic relations between diverse expressions of language, linguistic and world knowledge play an important role. In this article, we explore the effectiveness of different types of currently available resources including synonyms, antonyms, hypernym-hyponym relations, and lexical(More)
Classifying and identifying semantic relations between facts and opinions on the Web is of utmost importance for organizing information on the Web, however , this requires consideration of a broader set of semantic relations than are typically handled in Recognizing Tex-tual Entailment (RTE), Cross-document Structure Theory (CST), and similar tasks. In this(More)
u Our system learns plausible transformations of pairs of Text t 1 and Hypothesis t 2 only from semantic labels of the pairs using a discriminative probabilistic model combined with the framework of Natural Logic u We achieved the highest contradiction detection performance in MC subtask (28.57 of F1) (On June 29) (against reactivation of Oi Nuclear Power(More)