Papers with TF-IDF
Predicting Document Coverage for Relation Extraction (2022.tacl-1)
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| Challenge: | Existing methods for predicting document coverage for relation extraction (RE) are limited in their predictive power. |
| Approach: | They propose a task of predicting the coverage of a text document for relation extraction . they analyze a dataset of 31,366 diverse documents for 520 entities . |
| Outcome: | The proposed model achieves an F1 score of up to 46% on two use cases. |
Detection of Propaganda Using Logistic Regression (D19-50)
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| Challenge: | Various propaganda techniques are used to manipulate peoples perspectives to foster a predetermined agenda. |
| Approach: | They propose a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. |
| Outcome: | The proposed tool outperforms the baseline on linguistic and semantic features. |
NEAT-IR: Neural Explainable Analysis Tool for Information Retrieval (2026.acl-srw)
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| Challenge: | Neural IR models achieve strong performance but remain difficult to interpret. |
| Approach: | They propose a black-box analysis framework that explains ColBERT’s ranking behavior using 26 classical IR features. |
| Outcome: | The proposed framework preserves ColBERT’s rankings nearly perfectly, yet only explain R2 0.28 of score variance. |
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions (D19-57)
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| Challenge: | Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively. |
| Approach: | They propose to use attention-based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and to use BM25 and other relevance measures for re-ranking. |
| Outcome: | The proposed system scored 0.86 mAP and 0.58 macro average accuracy in the RDoC Tasks of BioNLP-OST 2019 . |
Enhancing Job Evaluation with Data Augmentation and Text Classification (2026.acl-industry)
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| Challenge: | Recruiters rely on job titles, role descriptions, and responsibility levels to determine job grades and salary structures. |
| Approach: | They propose to semi-automate job evaluation by fine-tuning a RoBERTa model for classification and using Gemini to generate synthetic job descriptions for rare job titles. |
| Outcome: | The proposed method improves job evaluation by boosting consistency and speeding up workflows. |
An LCF-IDF Document Representation Model Applied to Long Document Classification (2024.lrec-main)
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| Challenge: | Document representation models have been used for years in NLP and Text Mining tasks but are limited when it comes to capturing the deeper semantics and context of textual data. |
| Approach: | They propose to use a Latent Concept Frequency-Inverse Document Frequence model to exploit the advantages of TF-IDF while incorporating semantic context into the model. |
| Outcome: | The proposed model outperforms existing models on the Long Document Classification task and shows that it performs better than TF-IDF and BERT-like representation models. |
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization (2022.emnlp-main)
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| Challenge: | Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP. |
| Approach: | They propose an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoding model. |
| Outcome: | Empirically, for k > 10, our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods that re-rank items retrieved using a dual-encoder or TF-IDF. |
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers. |
| Approach: | They propose to efficiently remove poisoned examples before or during fine-tuning . |
| Outcome: | The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset. |
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index (2021.naacl-main)
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| Challenge: | Using the full vocabulary results in less explainable and memory intensive models. |
| Approach: | They propose a vocabulary selection method that views words as members of a team trying to maximize the model's performance. |
| Outcome: | The proposed method outperforms baseline models on multiple tasks and datasets. |
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing open-domain question answering systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps. |
| Approach: | They propose a sample-efficient method to pretrain the paragraph encoder using an existing pretraining model instead of heuristically created pseudo question-paragraph pairs. |
| Outcome: | The proposed method outperforms a strong dense retrieval baseline that uses 6 times more computation for training. |
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks (2020.coling-main)
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| Challenge: | Contextual embeddings have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity. |
| Approach: | They propose to integrate transformer-based neural language models into their models to achieve relative improvements of up to 36% on granular tasks. |
| Outcome: | The proposed model outperforms baselines for more granular tasks while outperforming TF-IDF for more complex tasks. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology (2020.lrec-1)
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| Challenge: | Existing ontologies for Arabic are difficult due to the lack of resources and the nature of the Arabic language. |
| Approach: | They propose to build an Arabic ontology that integrates scientific vocabularies with informal equivalents. |
| Outcome: | The proposed ontology integrates scientific vocabularies with informal equivalents in Arabic . it will be automatically generated but the results will be evaluated by a domain expert . |
Afaan Oromo Hate Speech Detection and Classification on Social Media (2022.lrec-1)
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| Challenge: | Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo. |
| Approach: | They develop a model to detect and classify Afaan Oromo hate speech on social media using different machine learning algorithms. |
| Outcome: | The proposed model outperforms existing models in gender, religion, race, and offensive speech on social media. |
TIDES: Technical Information Discovery and Extraction System (2025.emnlp-main)
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| Challenge: | Traditional pre-trained LLMs struggle with domain-specific terminology, while fine-tuned LLM requires substantial computational resources. |
| Approach: | They propose a training-free approach that combines TF-IDF with prompt-based LLMs to address technical questions. |
| Outcome: | The proposed system improves the accuracy and efficiency of QA systems in technical domains without LLM retraining. |