Search (8 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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Source

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

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Abstract

The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State-of-the-art approaches view it as an expert search or resource selection problem. We investigate the effect of content-based similarity between posts on the performance of the retrieval system. We test two different approaches for smoothing (regularizing) relevance scores of posts based on their dependencies. In the first approach, we smooth term distributions describing posts by performing a random walk over a document-term graph in which similar posts are highly connected. In the second, we directly smooth scores for posts using a regularization framework that aims to minimize the discrepancy between scores for similar documents. We then extend these approaches to consider the time interval between the posts in smoothing the scores. The idea is that if two posts are temporally close, then they are good sources for smoothing each other's relevance scores. We compare these methods with the state-of-the-art approaches in blog search that employ Language Modeling-based resource selection algorithms and fusion-based methods for aggregating post relevance scores. We show performance gains over the baseline techniques which do not take advantage of the relation between posts for smoothing relevance estimates.

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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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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