BITS Faculty Publications
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Item AENeT: an attention-enabled neural architecture for fake news detection using contextual features(Springer, 2021) Narang, Pratik; Sharma, YashvardhanIn the current era of social media, the popularity of smartphones and social media platforms has increased exponentially. Through these electronic media, fake news has been rising rapidly with the advent of new sources of information, which are highly unreliable. Checking off a particular news article is genuine or fake is not easy for any end user. Search engines like Google are also not capable of telling about the fakeness of any news article due to its restriction with limited query keywords. In this paper, our end goal is to design an efficient deep learning model to detect the degree of fakeness in a news statement. We propose a simple network architecture that combines the use of contextual embedding as word embedding and uses attention mechanisms with relevant metadata available. The efficacy and efficiency of our models are demonstrated on several real-world datasets. Our model achieved 46.36% accuracy on the LIAR dataset, which outperforms the current state of the art by 1.49%.Item Deep Learning Approaches for Question Answering System(Elsevier, 2018) Sharma, YashvardhanQuestion Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Consequently, the field is one of the most researched fields in computer science today. The last few years have seen considerable developments and improvement in the state of the art, much of which can be credited to upcoming of Deep Learning. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook.Item Composite Sequential Modeling for Identifying Fake Reviews(De Gruyter, 2018-04) Sharma, YashvardhanThis 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.Item Neural Network-Based Architecture for Sentiment Analysis in Indian Languages(De Gruyter, 2018-06) Sharma, YashvardhanSentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages (Hindi, Bengali, and Tamil). Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks (RNNs), long short-term memory (LSTM), convolutional neural network (CNN)] with optimum parameter settings (to avoid over-fitting and error accumulation). These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect the overall performance of the approach. A comparison has also been performed with existing approaches to find out if neural networks have an added advantage over traditional machine learning techniques.Item Bits_Pilani@INLI-FIRE-2017:Indian Native Language Identification using Deep Learning(CEUR, 2017) Sharma, YashvardhanThe task of Native Language Identification involves identifying the prior or first learnt language of a user based on his writing technique and/or analysis of speech and phonetics in second language. There is a surplus of such data present on social media sites and organised dataset from bodies like Educational Testing Service(ETS), which can be exploited to develop language learning systems and forensic linguistics. In this paper we propose a deep neural network for this task using hierarchical paragraph encoder with attention mechanism to identify relevant features over tendencies and errors a user makes with second language for the INLI task in FIRE 2017. The task involves six Indian languages as prior/native set and english as the second language which has been collected from user's social media account.Item Catchphrase Extraction from Legal Documents Using LSTM Networks(CEUR, 2017-12) Sharma, YashvardhanLegal texts usually have a complex structure and reading through them is a time-consuming and strenuous task. Hence it is essential to provide the legal practitioners a concise representation of the text. Catchphrases are those phrases which state the important issues present in the text, thus effectively characterizing it. This paper proposes an approach for the subtask 1 of the task IRLed (Information Retrieval from Legal Documents), FIRE 2017. The proposed algorithm uses a three step approach for extracting catchphrases from legal documents.Item Gender Identification in Russian Texts(CEUR, 2017-12) Sharma, YashvardhanThe last few years have seen a massive research related to automatic retrieving of information from the text, mainly the information about its author (authorship profiling) like gender, age etc. The automatic extraction of the information from text related to gender is essential to forensics, security, and marketing. For example, companies may be interested to learn about the gender of the people who likes or dislikes their products which can then be analyzed to know which section of the market is disliking their products. It helps in improving the sales of a company.