Faculty Publications

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Author: search.filters.author.Barathi Ganesh, H.B.

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    MedNLU: Natural Language Understander for Medical Texts
    (Springer Science and Business Media Deutschland GmbH, 2020) Barathi Ganesh, H.B.; Reshma, U.; Padannayil, K.P.; Anand Kumar, M.
    Natural Language Understanding is one of the essential tasks for building clinical text-based applications. Understanding of these clinical texts can be achieved through Vector Space Models and Sequential Modelling tasks. This paper is focused on sequential modelling i.e. Named Entity Recognition and Part of Speech Tagging by attaining a state of the art performance of 93.8% as F1 score for i2b2 clinical corpus and achieves 97.29% as F1 score for GENIA corpus. This paper also states the performance of feature fusion by integrating word embedding, feature embedding and character embedding for sequential modelling tasks. We also propose a framework based on a sequential modelling architecture, named MedNLU, which has the capability of performing Part of Speech Tagging, Chunking, and Entity Recognition on clinical texts. The sequence modeler in MedNLU is an integrated framework of Convolutional Neural Network, Conditional Random Fields and Bi-directional Long-Short Term Memory network. © 2020, Springer Nature Switzerland AG.
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    Indian native language identification - INLI 2018
    (Association for Computing Machinery acmhelp@acm.org, 2018) Anand Kumar, M.; Barathi Ganesh, H.B.; Padannayil, K.P.; Ajay, S.G.
    The growth of digital platforms enables the industries to serve user specific services. Most of the time, the information of the internet users are not explicitly available and it acts as a constrain in developing the personalized applications. There comes the need for author profiling tasks, which intends to predict the internet users characteristics from their texts. Native language Identification is one among the author profiling task, that predicts the authors native language from their texts available in other language. We have proposed Indian Native Language Identification task, where the internet users texts are written in English and participants needs to find, whether the user’s native language is from Tamil, Malayalam, Kannada, Telugu, Bengali and Hindi. The corpus is collected from texts from regional news paper pages available in Facebook by considering the hypothesis that the user belongs to a particular region will read the news from respective regional news paper. © 2018 Association for Computing Machinery.