Search (9 results, page 1 of 1)

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Abstract

Explains briefly what constitutes the imaging process and explains how imaging can be used in information retrieval. Proposes an approach based on the concept of: 'a term is a possible world'; which enables the exploitation of term to term relationships which are estimated using an information theoretic measure. Reports results of an evaluation exercise to compare the performance of imaging retrieval, using possible world semantics, with a benchmark and using the Cranfield 2 document collection to measure precision and recall. Initially, the performance imaging retrieval was seen to be better but statistical analysis proved that the difference was not significant. The problem with imaging retrieval lies in the amount of computations needed to be performed at run time and a later experiement investigated the possibility of reducing this amount. Notes lines of further investigation

Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

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Abstract

The evaluation of an implication by imaging is a logical technique developed in the framework of modal logic. Its interpretation in the context of a 'possible worlds' semantics is very appealing for information retrieval. In 19889, Van Rijsbergen suggested its use for solving 1 of the fundamental problems of logical models of information retrieval: the evaluation of the logical implication that a document is relevant to a query if it implies the query. Since then, others have tried to follow that suggestion proposing models and applications, though without much success. Most of these approaches had as their basic assunption the consideration that ' document is a possible world'. Proposes instead an approach based on a completely different assumption: ' a term is a possible world'. This approach enables the exploitation of term-term relationships which are estimated using an information theoretic measure

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Abstract

Fake news is considered one of the main threats of our society. The aim of fake news is usually to confuse readers and trigger intense emotions to them in an attempt to be spread through social networks. Even though recent studies have explored the effectiveness of different linguistic patterns for fake news detection, the role of emotional signals has not yet been explored. In this paper, we focus on extracting emotional signals from claims and evaluating their effectiveness on credibility assessment. First, we explore different methodologies for extracting the emotional signals that can be triggered to the users when they read a claim. Then, we present emoCred, a model that is based on a long-short term memory model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. In addition, we perform an analysis to understand which emotional signals and which terms are the most useful for the different credibility classes. We conduct extensive experiments and a thorough analysis on real-world datasets. Our results indicate the importance of incorporating emotional signals in the credibility assessment problem.

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Source

Journal of information science. 29(2003) no.2, S.87-96

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Date

22. 3.2003 19:27:36

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Date

5. 6.2006 11:22:23

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Date

29. 7.2008 19:35:12

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Date

29. 9.2018 13:24:44