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Proactive information retrieval
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Sen, Procheta. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
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In: Sen, Procheta (2021) Proactive information retrieval. PhD thesis, Dublin City University. (2021)
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Tempo-lexical context driven word embedding for cross-session search task extraction
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In: Sen, Procheta, Ganguly, Debasis orcid:0000-0003-0050-7138 and Jones, Gareth J.F. orcid:0000-0003-2923-8365 (2018) Tempo-lexical context driven word embedding for cross-session search task extraction. In: 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1-6 June 2018, New Orleans, LA, USA. (2018)
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Joint estimation of topics and hashtag relevance in cross-lingual tweets
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In: Sen, Procheta, Ganguly, Debasis orcid:0000-0003-0050-7138 and Jones, Gareth J.F. orcid:0000-0003-2923-8365 (2016) Joint estimation of topics and hashtag relevance in cross-lingual tweets. In: ACM on International Conference on the Theory of Information Retrieval, ICTIR 2016, 12- 6 Sept 2016., Newark, DE, USA. ISBN 978-1-4503-4497-5 (2016)
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Abstract:
Twitter is a widely used platform for sharing news articles. An emerging trend in multi-lingual communities is to share non-English news articles using English tweets in order to spread the news to a wider audience. In general, the choice of relevant hashtags for such tweets depends on the topic of the non-English news article. In this paper, we address the problem of automatically detecting the relevance of the hashtags of such tweets. More specifically, we propose a generative model to jointly model the topics within an English tweet and those within the non-English news article shared from it to predict the relevance of the hashtags of the tweet. For conducting experiments, we compiled a collection of English tweets that share news articles in Bengali (a South Asian language). Our experiments on this dataset demonstrate that this joint estimation based approach using the topics from both the non-English news articles and the tweets proves to be more effective for relevance estimation than that of only using the topics of a tweet itself.
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Keyword:
bilingual topic modelling; Cross-lingual Tweet tagging; joint estimation of topic and tag relevance
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URL: http://doras.dcu.ie/23387/
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