Context Aware News Similarity for the identification of follow-ups among human loss related news articles

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dc.contributor.authorWaqas Ali
dc.date.accessioned2019-01-16T06:29:26Z
dc.date.available2019-01-16T06:29:26Z
dc.date.issued2018
dc.descriptionDr. Adnan Abiden_US
dc.description.abstractText similarity plays an important role in document clustering, plagiarism detection, automatic student answer grading, information retrieval and language translation systems. Many researchers have studied on string, corpus and knowledge based approaches to resolve the problem of document similarity. In this paper research has been made on full text similarity and context information of a news. Time complexity and follow-ups distribution are main challenges for full text similarity which is computed by using jaccard index. The proposed system in this paper uses context information of news to resolve these issue and to classify the news into three categories i.e. same day follow-ups, different day follow-ups and distinct news. Context is built by using well known Stanford parser which further enriched with factor of severity to reduce the target dataset for similarity computation. Results showed that context aware similarity approach is better than traditional full text similarity approach in efficiency and accuracy.en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/3531
dc.language.isoenen_US
dc.publisherUniversity of Management & Technologyen_US
dc.subjectContext Aware News , news articles, human lossen_US
dc.subjectMSen_US
dc.titleContext Aware News Similarity for the identification of follow-ups among human loss related news articlesen_US
dc.titleContext aware news similarity for the identification of follow-ups among human loss related news articlesen_us
dc.typeThesisen_US
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