no code implementations • COLING 2022 • Andrew Schneider, Lihong He, Zhijia Chen, Arjun Mukherjee, Eduard Dragut
Word embedding models only include terms that occur a sufficient number of times in their training corpora.
no code implementations • RANLP 2021 • Fan Yang, Eduard Dragut, Arjun Mukherjee
Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim.
no code implementations • RANLP 2021 • Marjan Hosseinia, Eduard Dragut, Dainis Boumber, Arjun Mukherjee
We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting.
no code implementations • 21 Jul 2023 • Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings.
no code implementations • 1 May 2023 • Sadat Shahriar, Arjun Mukherjee, Omprakash Gnawali
In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets.
1 code implementation • RANLP 2021 • Kishore Tumarada, Yifan Zhang, Fan Yang, Eduard Dragut, Omprakash Gnawali, Arjun Mukherjee
Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
no code implementations • RANLP 2021 • Amartya Hatua, Arjun Mukherjee, Rakesh M. Verma
This article describes research on claim verification carried out using a multiple GAN-based model.
no code implementations • 12 Mar 2021 • Yifan Zhang, Dainis Boumber, Marjan Hosseinia, Fan Yang, Arjun Mukherjee
It is also one of the first to use Deep Language Models in this setting.
no code implementations • 15 Dec 2020 • Yifan Zhang, Fan Yang, Marjan Hosseinia, Arjun Mukherjee
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM).
no code implementations • COLING 2020 • Fan Yang, Eduard Dragut, Arjun Mukherjee
We evaluate the proposed model on this dataset.
1 code implementation • 29 Aug 2020 • Md. Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour
A popular approach in representing source code is neural source code embeddings that represents programs with high-dimensional vectors computed by training deep neural networks on a large volume of programs.
no code implementations • 14 Aug 2020 • Lihong He, Chen Shen, Arjun Mukherjee, Slobodan Vucetic, Eduard Dragut
We show that the early arrival rate of comments is the best indicator of the eventual number of comments.
no code implementations • 1 Aug 2020 • Daniel Lee, Rakesh Verma, Avisha Das, Arjun Mukherjee
In this paper, we revisit the challenging problem of unsupervised single-document summarization and study the following aspects: Integer linear programming (ILP) based algorithms, Parameterized normalization of term and sentence scores, and Title-driven approaches for summarization.
no code implementations • 4 Jul 2020 • Yigeng Zhang, Fan Yang, Yifan Zhang, Eduard Dragut, Arjun Mukherjee
In this work, we propose a method that differentiates the satirical news and true news.
1 code implementation • WS 2020 • Marjan Hosseinia, Eduard Dragut, Arjun Mukherjee
We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues.
no code implementations • 7 Feb 2019 • Abhishek Laddha, Arjun Mukherjee
The attention mechanism captures the importance of context words on a particular aspect opinion expression when multiple aspects are present in a sentence via location and content based memory.
no code implementations • COLING 2018 • Sohan De Sarkar, Fan Yang, Arjun Mukherjee
Satirical news detection is important in order to prevent the spread of misinformation over the Internet.
no code implementations • 17 Mar 2018 • Marjan Hosseinia, Arjun Mukherjee
We consider the authorship verification problem for both small and large scale datasets.
1 code implementation • EMNLP 2017 • Fan Yang, Arjun Mukherjee, Eduard Dragut
Satirical news is considered to be entertainment, but it is potentially deceptive and harmful.
no code implementations • 11 Apr 2017 • K. C. Santosh, Suman Kalyan Maity, Arjun Mukherjee
We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media.
no code implementations • 9 Mar 2017 • Marjan Hosseinia, Arjun Mukherjee
This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches.
no code implementations • COLING 2016 • Fan Yang, Arjun Mukherjee, Yifan Zhang
In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features.