Eduard Dragut


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DMDD: A Large-Scale Dataset for Dataset Mentions Detection
Huitong Pan | Qi Zhang | Eduard Dragut | Cornelia Caragea | Longin Jan Latecki
Transactions of the Association for Computational Linguistics, Volume 11

The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are limited in size and naming diversity. In this paper, we introduce the Dataset Mentions Detection Dataset (DMDD), the largest publicly available corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219 scientific articles with over 449,000 dataset mentions weakly annotated in the format of in-text spans, and an evaluation set, which comprises 450 scientific articles manually annotated for evaluation purposes. We use DMDD to establish baseline performance for dataset mention detection and linking. By analyzing the performance of various models on DMDD, we are able to identify open problems in dataset mention detection. We invite the community to use our dataset as a challenge to develop novel dataset mention detection models.


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COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings
Andrew Schneider | Lihong He | Zhijia Chen | Arjun Mukherjee | Eduard Dragut
Proceedings of the 29th International Conference on Computational Linguistics

Social media is the ultimate challenge for many natural language processing tools. The constant emergence of linguistic constructs challenge even the most sophisticated NLP tools. Predicting word embeddings for out of vocabulary words is one of those challenges. Word embedding models only include terms that occur a sufficient number of times in their training corpora. Word embedding vector models are unable to directly provide any useful information about a word not in their vocabularies. We propose a fast method for predicting vectors for out of vocabulary terms that makes use of the surrounding terms of the unknown term and the hidden context layer of the word2vec model. We propose this method as a strong baseline in the sense that 1) while it does not surpass all state-of-the-art methods, it surpasses several techniques for vector prediction on benchmark tasks, 2) even when it underperforms, the margin is very small retaining competitive performance in downstream tasks, and 3) it is inexpensive to compute, requiring no additional training stage. We also show that our technique can be incorporated into existing methods to achieve a new state-of-the-art on the word vector prediction problem.

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Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)
Eduard Dragut | Yunyao Li | Lucian Popa | Slobodan Vucetic | Shashank Srivastava
Proceedings of the Fourth 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.

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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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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 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.

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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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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 A screencast of DebugSL can be viewed at


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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.


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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)


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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)


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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)