Surveys in countries at all stages of development have founded their work on health-status and morbidity, on self-reported health status by individual members of households who feel sick. Doubts have been raised related to cross-population comparisons on the objectivity of a person's judgement of his/her health. Amartya Sen (Objectivity and position, University of Kansas, Department of Philosophy, Kansas, 1992, Philos Public Affair 126-145, 1993 has written on the philosophy of objectivity and, in Sen (Br Med J 324:860, 2002), compared morbidity data across Indian States, and countries like the United States. His discussion helps formulating and testing a null hypothesis that an Individual's self-reported health-status (SRH) and morbidity (SRM) do not depend on his/her socio-economic status (SES) as well the socio-economic environment in which he/she lives. The test rejects the null hypothesis in favour of an alternative that there is a positive association between the two using data from the 71st Round (January-June 2014) survey of the National Sample Survey Office (NSSO). This means that lower the SES, the lower will be the health-status (reported as having higher morbidity); the higher the SES, higher will be the health-status (reported as having low morbidity). We also explore a linear probability model with constraints on the error term for ensuring that the estimated probabilities lie within the closed unit interval [0, 1].
This paper describes our approach to SemEval-2018 Task 7-given an entitytagged text from the ACL Anthology corpus, identify and classify pairs of entities that have one of six possible semantic relationships. Our model consists of a convolutional neural network leveraging pre-trained word embeddings, unlabeled ACL-abstracts, and multiple window sizes to automatically learn useful features from entity-tagged sentences. We also experiment with a hybrid loss function, a combination of cross-entropy loss and ranking loss, to boost the separation in classification scores. Lastly, we include WordNet-based features to further improve the performance of our model. Our best model achieves an F1(macro) score of 74.2 and 84.8 on subtasks 1.1 and 1.2, respectively.
This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions were paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.
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