Eduard Dragut


2021

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Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
Eduard Dragut | Yunyao Li | Lucian Popa | Slobodan Vucetic
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

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On the Usefulness of Personality Traits in Opinion-oriented Tasks
Marjan Hosseinia | Eduard Dragut | Dainis Boumber | Arjun Mukherjee
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Cafe forum. We induce personality embeddings from our transformer-based model and investigate if they can be used for downstream text classification tasks. Experimental evidence shows that personality embeddings are effective in three classification tasks including authorship verification, stance, and hyperpartisan detection. We also provide novel and interpretable analysis for the third task: hyperpartisan news classification.

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Opinion Prediction with User Fingerprinting
Kishore Tumarada | Yifan Zhang | Fan Yang | Eduard Dragut | Omprakash Gnawali | Arjun Mukherjee
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user’s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user’s comments conditioned on relevant user’s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13% improvement in micro F1-score compared to previous approaches. 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.

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Improving Evidence Retrieval with Claim-Evidence Entailment
Fan Yang | Eduard Dragut | Arjun Mukherjee
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Claim verification is challenging because it requires first to find textual evidence and then apply claim-evidence entailment to verify a claim. Previous works evaluate the entailment step based on the retrieved evidence, whereas we hypothesize that the entailment prediction can provide useful signals for evidence retrieval, in the sense that if a sentence supports or refutes a claim, the sentence must be relevant. We propose a novel model that uses the entailment score to express the relevancy. Our experiments verify that leveraging entailment prediction improves ranking multiple pieces of evidence.

2020

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Predicting Personal Opinion on Future Events with Fingerprints
Fan Yang | Eduard Dragut | Arjun Mukherjee
Proceedings of the 28th International Conference on Computational Linguistics

Predicting users’ opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population’s opinion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user’s future opinion. We introduce a recurrent neural network based model that integrates fingerprinting. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach.

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Stance Prediction for Contemporary Issues: Data and Experiments
Marjan Hosseinia | Eduard Dragut | Arjun Mukherjee
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.

2018

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Regular Expression Guided Entity Mention Mining from Noisy Web Data
Shanshan Zhang | Lihong He | Slobodan Vucetic | Eduard Dragut
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many important entity types in web documents, such as dates, times, email addresses, and course numbers, follow or closely resemble patterns that can be described by Regular Expressions (REs). Due to a vast diversity of web documents and ways in which they are being generated, even seemingly straightforward tasks such as identifying mentions of date in a document become very challenging. It is reasonable to claim that it is impossible to create a RE that is capable of identifying such entities from web documents with perfect precision and recall. Rather than abandoning REs as a go-to approach for entity detection, this paper explores ways to combine the expressive power of REs, ability of deep learning to learn from large data, and human-in-the loop approach into a new integrated framework for entity identification from web data. The framework starts by creating or collecting the existing REs for a particular type of an entity. Those REs are then used over a large document corpus to collect weak labels for the entity mentions and a neural network is trained to predict those RE-generated weak labels. Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents. The experimental evaluation on several entity identification problems shows that the proposed framework achieves impressive accuracy, while requiring very modest human effort.

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DebugSL: An Interactive Tool for Debugging Sentiment Lexicons
Andrew Schneider | John Male | Saroja Bhogadhi | Eduard Dragut
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We introduce DebugSL, a visual (Web) debugging tool for sentiment lexicons (SLs). Its core component implements our algorithms for the automatic detection of polarity inconsistencies in SLs. An inconsistency is a set of words and/or word-senses whose polarity assignments cannot all be simultaneously satisfied. DebugSL finds inconsistencies of small sizes in SLs and has a rich user interface which helps users in the correction process. The project source code is available at https://github.com/atschneid/DebugSL A screencast of DebugSL can be viewed at https://cis.temple.edu/~edragut/DebugSL.webm

2017

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Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Fan Yang | Arjun Mukherjee | Eduard Dragut
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.

2015

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Towards Debugging Sentiment Lexicons
Andrew Schneider | Eduard Dragut
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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The Role of Adverbs in Sentiment Analysis
Eduard Dragut | Christiane Fellbaum
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014)

2012

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Polarity Consistency Checking for Sentiment Dictionaries
Eduard Dragut | Hong Wang | Clement Yu | Prasad Sistla | Weiyi Meng
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)