Papers by Eduard Dragut
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)
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| Challenge: | Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content. |
| Approach: | They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification. |
| Outcome: | The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions. |
CentaurTA: A Self-Improving Human-Agents Collaboration Framework for Thematic Analysis (2026.findings-acl)
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| Challenge: | Existing large language model approaches for qualitative analysis are labor-intensive and costly. |
| Approach: | They propose an iterative human–agent framework for scalable thematic analysis that integrates structured human feedback with rubric-based evaluation. |
| Outcome: | The proposed framework improves coding alignment and transparency across multiple datasets, baselines, and LLM families. |
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. |
| Approach: | They evaluate various LLMs across multiple domains using hundreds of ICL examples and then assess the feasibility of using many-shot ICL as a data annotation framework. |
| Outcome: | The proposed framework can be scaled to hundreds of examples and annotate and refining data for low-resource NER tasks. |
SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents (2024.emnlp-main)
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| Challenge: | Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data. |
| Approach: | They propose to use a scientific entity and relation extraction dataset to capture interactions between entities in full texts. |
| Outcome: | The proposed dataset captures the intricate use and interactions among entities in full texts and provides an out-of-distribution test set to offer a more realistic evaluation. |
DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition (2025.findings-naacl)
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| Challenge: | Existing methods to identify entities using distant annotations are expensive and time-consuming. |
| Approach: | They propose a training dynamics-based label cleaning approach to characterize distant annotations and an automatic threshold estimation strategy to locate errors in distant labels. |
| Outcome: | The proposed method outperforms several advanced DS-NER approaches across four datasets. |
The Overlooked Repetitive Lengthening Form in Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies have focused on emphatic and expressive language models with informal styles, such as memes and emojis. |
| Approach: | They propose a two-stage Explainable Instruction Tuning framework that can improve LLMs' performance and explainability for RLF with limited samples. |
| Outcome: | The proposed framework can match zero-shot GPT-4 in performance but not explainability for RLF with limited samples. |
Taxonomy-Driven Knowledge Graph Construction for Domain-Specific Scientific Applications (2025.findings-acl)
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| Challenge: | Existing methods for constructing domain-specific knowledge graphs neglect curated taxonomies and LLMs fail to extract KGs in specialized domains. |
| Approach: | They propose a taxonomy-driven framework for constructing domain-specific knowledge graphs . they use structured taxonomies, Large Language Models and Retrieval-Augmented Generation . |
| Outcome: | The proposed framework can be adapted for other specialized domains. |
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings (2022.coling-1)
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| Challenge: | Word embedding models only include terms that occur a sufficient number of times in training corpora. |
| Approach: | They propose a method for predicting word embeddings for out of vocabulary terms using word2vec. |
| Outcome: | The proposed method surpasses several methods on benchmark tasks and is inexpensive to compute. |
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions (2024.lrec-main)
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| Challenge: | SciDMT is an enhanced and expanded corpus for scientific mention detection . existing corpora are limited by their small volume and entity linking capabilities . |
| Approach: | They propose to enhance SciDMT, an annotated scientific corpus for scientific mention detection. |
| Outcome: | The proposed corpus is the largest for scientific entity mention detection . it is based on deep learning architectures like SciBERT and GPT-3.5 . |
DMDD: A Large-Scale Dataset for Dataset Mentions Detection (2023.tacl-1)
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| Challenge: | Existing corpora for dataset mention detection are limited in size and naming diversity. |
| Approach: | They propose a dataset for dataset mention detection that is the largest publicly available corpus for this task. |
| Outcome: | The proposed dataset is the largest publicly available corpus for dataset mention detection . it identifies open problems in dataset mention recognition and linking . |
UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies (2025.findings-acl)
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| Challenge: | Current research on edit intentions lacks a comprehensive edit intention taxonomy (EIT) that spans multiple application domains. |
| Approach: | They propose a Unified edit intention taxonomy that integrates existing edit intentions and integrates them into a comprehensive edit intention Taxonomic. |
| Outcome: | The proposed taxonomy achieves higher inter-annotator agreement scores compared to existing taxonomies and is applicable to a large set of application domains. |
Predicting Personal Opinion on Future Events with Fingerprints (2020.coling-main)
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| Challenge: | Existing methods to predict users’ opinions on going events may not be able to acquire such content and thus cannot infer an unbiased opinion on emerging events. |
| Approach: | They propose to model opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. |
| Outcome: | The proposed model can predict user’s opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. |
DebugSL: An Interactive Tool for Debugging Sentiment Lexicons (N18-5)
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| Challenge: | Existing studies have shown that polarity disagreements between SLs can negatively impact SA tasks. |
| Approach: | They propose to use a visual debugging tool for sentiment lexicons to detect polarity inconsistencies in SLs. |
| Outcome: | The proposed tool detects inconsistencies of small sizes and has a rich user interface which helps users in the correction process. |