Organizing information on the Web to support user judgments on information credibility

Akamine, Susumu; Kawahara, Daisuke; Kato, Yukie; Nakagawa, Tatsunori; Leon-Suematsu, Yutaka I.; Kawada, Takuya; Inui, Kentaro; Kurohashi, Sadao; Kidawara, Yutaka

doi:10.1109/iucs.2010.5666759

Cited by 8 publications

(2 citation statements)

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“…This section describes our hypothesis candidate generation, which replaces the noun pair (the cause and effect nouns) of an input event causality with another noun pair of the same semantic relation (Figure 1). First, we prepare a semantic relation database that records which binary pattern, e.g., A CAUSES B, which indicates a semantic relation, co-occurs with which noun pairs in 600 million web pages (Akamine et al 2010). 1 We prepared seven types of binary patterns based on previous work (Hashimoto et al 2014) 2012)'s method 2 and manual annotation for their method's results.…”

Section: Hypothesis Candidate Generationmentioning

confidence: 99%

Generating Event Causality Hypotheses through Semantic Relations

Hashimoto

Torisawa

Kloetzer

et al. 2015

AAAI

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Event causality knowledge is indispensable for intelligent natural language understanding. The problem is that any method for extracting event causalities from text is insufficient; it is likely that some event causalities that we can recognize in this world are not written in a corpus, no matter its size. We propose a method of hypothesizing unseen event causalities from known event causalities extracted from the web by the semantic relations between nouns. For example, our method can hypothesize "deploy a security camera" -> "avoid crimes" from "deploy a mosquito net" -> "avoid malaria" through semantic relation . Our experiments show that, from 2.4 million event causalities extracted from the web, our method generated more than 300,000 hypotheses, which were not in the input, with 70% precision. We also show that our method outperforms a state-of-the-art hypothesis generation method.

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Section: Hypothesis Candidate Generationmentioning

confidence: 99%

Generating Event Causality Hypotheses through Semantic Relations

Hashimoto

Torisawa

Kloetzer

et al. 2015

AAAI

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“…We show examples of online systems that utilize TSUBAKI as a search engine infrastructure. WISDOM [11]: A system that helps its users to judge the credibility of information on the Web by providing various information such as information sender and major and contradicting statements in documents related to a given query. Statement Map [12]: A system that allows its users to easily grasp relations, such as synonymous and antonymous relations, between statements described in documents in a search result.…”

Section: Cachementioning

confidence: 99%

TSUBAKI: An Open Search Engine Infrastructure for Developing Information Access Methodology

Shinzato

Shibata

Kawahara

et al. 2012

Journal of Information Processing

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Due to the explosive growth in the amount of information in the last decade, it is getting extremely harder to obtain necessary information by conventional information access methods. Hence, creation of drastically new technology is needed. For developing such new technology, search engine infrastructures are required. Although the existing search engine APIs can be regarded as such infrastructures, these APIs have several restrictions such as a limit on the number of API calls. To help the development of new technology, we are running an open search engine infrastructure, TSUBAKI, on a high-performance computing environment. In this paper, we describe TSUBAKI infrastructure.

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Comparing Intention and Behavior: Characteristics of Valuable Information and Information Verification

Thongmak¹

2016

2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing,

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Organizing information on the Web to support user judgments on information credibility

Cited by 8 publications

References 10 publications

Generating Event Causality Hypotheses through Semantic Relations

Generating Event Causality Hypotheses through Semantic Relations

TSUBAKI: An Open Search Engine Infrastructure for Developing Information Access Methodology

Comparing Intention and Behavior: Characteristics of Valuable Information and Information Verification

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