Mandeep Kaur


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Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data
Mandeep Kaur | Diego Mollá
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available online. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation.


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Temporal Orientation of Tweets for Predicting Income of Users
Mohammed Hasanuzzaman | Sabyasachi Kamila | Mandeep Kaur | Sriparna Saha | Asif Ekbal
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Automatically estimating a user’s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.