Pranjal Singh

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Despite the success of distributional semantics, composing phrases from word vectors remains an important challenge. Several methods have been tried for benchmark tasks such as sentiment classification, including word vector averaging, matrix-vector approaches based on parsing, and on-the-fly learning of paragraph vectors. Most models usually omit stop(More)
Salmonella is a diverse foodborne pathogen, which has more than 2600 recognized serovars. Classification of Salmonella isolates into serovars is essential for surveillance and epidemiological investigations; however, determination of Salmonella serovars, by traditional serotyping, has some important limitations (e.g. labor intensive, time consuming). To(More)
Tonometry-based devices are valuable method for vascular function assessment and for measurement of blood pressure. However current design and calibration methods rely on simple models, neglecting key geometrical features, and anthropometric and property variability among patients. Understanding impact of these influences on tonometer measurement is thus(More)
Word embeddings have the power to capture semantics.They have potential to represent syntax and semantics both. We have many sources of unsupervised raw data but not supervised data. Unsupervised techniques could greatly improve existing supervised (Collobert et al.(2013)). By leveraging large amount of data floating around, we can improve existing systems.(More)
In most of the languages today, the sequence of words is quite restricted. It is quite obvious that an underlying structure which is, in general, abstract, generates such word orders. This underlying structure is basically the syntax of the language. Also, it is shown in previous researches that syntactic features are the most informative features in(More)
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