Výsledky vyhledávání - "Lalita Bhanu Murthy"

Akademický článek

Aspect-Based Sentiment Classification Using Interactive Gated Convolutional Network

Autor: Avinash Kumar, Vishnu Teja Narapareddy, Veerubhotla Aditya Srikanth, Lalita Bhanu Murthy Neti, Aruna Malapati

Publikováno v: IEEE Access, Vol 8, Pp 22445-22453 (2020)

Aspect-based sentiment classification aims to detect the sentiment polarity of a target in a given context. Most previous approaches use long short-term memory (LSTM) and attention mechanisms to predict the sentiment polarity of targets, which are us

Externí odkaz: https://doaj.org/article/1855378a7ed4462fa0ab1bbdd895649d

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Akademický článek

Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM

Autor: Avinash Kumar, Vishnu Teja Narapareddy, Veerubhotla Aditya Srikanth, Aruna Malapati, Lalita Bhanu Murthy Neti

Publikováno v: IEEE Access, Vol 8, Pp 6388-6397 (2020)

Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Sarcasm detection is considered a binary cl

Externí odkaz: https://doaj.org/article/c0d7c75707604f9a8f9705e23ddf4c5b

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Akademický článek

Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features

Autor: SaiKiranmai Gorla, Lalita Bhanu Murthy Neti, Aruna Malapati

Publikováno v: Information, Vol 11, Iss 2, p 82 (2020)

Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for South-East Asian languages like T

Externí odkaz: https://doaj.org/article/d6ae07b19393401b894d43ecceeba9f4

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Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell

Autor: Gupta, Himanshu, Gulanikar, Abhiram Anand, Kumar, Lov, Neti, Lalita Bhanu Murthy

A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development phase. Studies observe that code smells made the code more susceptible

Externí odkaz: http://arxiv.org/abs/2108.04656

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An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

Autor: Gupta, Himanshu, Kulkarni, Tanmay G., Kumar, Lov, Neti, Lalita Bhanu Murthy, Krishna, Aneesh

Publikováno v: Lecture Notes in Networks and Systems, vol 226. Springer, Cham. 27 April 2021, pages 120-132

Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is prominent to e

Externí odkaz: http://arxiv.org/abs/2108.04659

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