Department of Computer Science and Information Systems

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Author: search.filters.author.Sharma, YashvardhanSubject: search.filters.subject.Machine Learning

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Now showing 1 - 4 of 4
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    FAID: Feature Aftermath for Irony Discernment
    (IEEE, 2019) Sharma, Yashvardhan
    This paper deals with the impediment of identifying sarcasm in social media text which can be used to improve sentiment analysis technique. After thorough analysis, some features were identified which could help in recognition of sarcasm. In state of art, features have been extracted from the data set which embraced standalone sentences. Proposed algorithm analyzes the impact of these features and a combination of them on the review data set in which reviews had three or more sentences, so that context of sentence is also taken into consideration by the machine before classifying a review.
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    Composite Sequential Modeling for Identifying Fake Reviews
    (De Gruyter, 2018-04) Sharma, Yashvardhan
    This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques.
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    Modeling Classifier for Code Mixed Cross Script Questions
    (CEUR, 2016) Sharma, Yashvardhan
    With a boom in the internet, the social media text had been increasing day by day and the user generated content (such as tweets and blogs) in Indian languages are written using Roman script due to various socio-cultural and technological reasons. A majority of these posts are multilingual in nature and many involve code mixing where lexical items and gram- matical features from two languages appear in one sentence. Focusing on this current multilingual scenario, code-mixed cross-script (i.e., non-native script) data gives rise to a new problem and presents serious challenges to automatic Ques- tion Answering (QA) and for this question classi cation will be required which is an important step towards QA. This paper proposes an approach to handle cross script question classi cation as it is an important task of question analysis which detects the category of the question.
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    Query Labelling for Indic Languages using a hybrid approach
    (CEUR, 2015) Sharma, Yashvardhan
    With a boom in the internet, social media text has been increasing day by day. Much of the user generated content on internet is written in a very informal way. Usually people tend to write text on social media using indigenous script. To understand a script different from ours is a difficult task. Moreover, nowadays queries received by the search engines are large number of transliterated text. Hence providing a common platform to deal with the problem of transliterated text becomes really important. This paper presents our approach to handle labeling of queries as part of the FIRE2015 shared task on Mixed-Script Information Retrieval. Tokens in the query are labeled on basis of a hybrid approach which involves rule based and machine learning techniques. Each annotation has been dealt separately but sequentially.