Papers with supervised classifiers
Content-Based Conflict of Interest Detection on Wikipedia (L18-1)
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| Challenge: | Conflict-of-Interest (CoI) editing is a problem on Wikipedia that is highly subjective . a key feature of Wiki sites is to allow people from all over the world to add or modify articles anonymously and without consequence. |
| Approach: | They frame CoI detection as a binary classification problem and explore features for it . they find that stylometric features outperform other types of features and give an F-measure of 0.63 . |
| Outcome: | The proposed method outperforms other features and gives an F-measure of 0.63 . the proposed method is not certain that the set of non-CoI articles contains any CoI articles . |
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (2021.tacl-1)
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| Challenge: | Recent studies have focused on training complex neural networks on labeled data. |
| Approach: | They propose to use logical mechanisms to classify argumentative relations without training on labeled data. |
| Outcome: | The proposed method classifies argumentative relations without training on labeled data significantly better than unsupervised baselines. |
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (C18-1)
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| Challenge: | Existing domain adaptation methods for sentiment analysis are sensitive to domain differences, resulting in classifiers that perform poorly on new domains. |
| Approach: | They propose a domain adaptation problem as an embedding projection task using two mono-domain embeddable spaces and a bi-domain space to project across domains and predict sentiment. |
| Outcome: | The proposed model performs better on domains similar to state-of-the-art methods while requiring longer training times. |
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models (2020.coling-main)
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| Challenge: | Existing crowdsourcing platforms do not support sentiment analysis for Amharic, and there are no expert researchers in the area. |
| Approach: | They propose to build a social-network-friendly Amharic sentiment analysis tool using the Telegram bot and collect 9.4k tweets where each tweet is annotated by three Telegram users. |
| Outcome: | The proposed system outperforms existing classifiers in Amharic and other low-resource languages due to the widespread use of sarcasm and figurative speech. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Analysis of Automated Document Relevance Annotation for Information Retrieval in Oil and Gas Industry (2025.emnlp-industry)
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João Vitor Mariano Correia, Murilo Missano Bell, João Vitor Robiatti Amorim, Jonas Queiroz, Daniel Pedronette, Ivan Rizzo Guilherme, Felipe Lima de Oliveira
| Challenge: | Lack of high-quality test collections challenges Information Retrieval (IR) in specialized domains. |
| Approach: | They compare supervised classifiers against zero-shot Large Language Models for automated relevance annotation in the oil and gas industry using human expert judgments as a benchmark. |
| Outcome: | The proposed classifier outperforms LLMs in the oil and gas industry using human expert judgments. |
Playing the Part of the Sharp Bully: Generating Adversarial Examples for Implicit Hate Speech Detection (2023.findings-acl)
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| Challenge: | Existing algorithms for hate speech detection focus on explicit forms of hate speech, but they fail to properly detect subtle and implicit HS messages. |
| Approach: | They propose a framework for generating adversarial implicit HS short-text messages using Auto-regressive language models and a strategy to group the generated messages in complexity levels. |
| Outcome: | The proposed framework shows that iteratively retraining on HARD messages significantly improves implicit HS benchmarks. |
Modeling Deliberative Argumentation Strategies on Wikipedia (P18-1)
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| Challenge: | Existing models for deliberative discussions have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. |
| Approach: | They propose to model argumentation strategies of deliberative discussions by annotating ongoing discussions with a label that can be used for move description. |
| Outcome: | The proposed model can predict arguments of participants in deliberative discussions using metadata from Wikipedia talk pages. |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection (2020.acl-main)
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| Challenge: | supervised hypernymy detection has been studied under various frameworks . supervised classifiers are more likely to suffer from "lexical memorization" |
| Approach: | They propose a representation learning framework called Bidirectional Residual Relation Embeddings to model the possibility of a term being mapped to another in the embedding space by hypernymy relations. |
| Outcome: | The proposed model outperforms baselines over evaluation frameworks. |
Discovering the Language of Wine Reviews: A Text Mining Account (L18-1)
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| Challenge: | odors and flavors are often expressed in wine reviews, but they are often not. |
| Approach: | They use a corpus of wine reviews to find out what wine is like in a review . they use lexical bag-of-words features, domain-specific terminology features and word embedding features to train machine learning. |
| Outcome: | The proposed model predicts the wine's color, grape variety, and country of origin based on the review text alone. |
Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer’s Disease Detection (2025.acl-long)
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| Challenge: | Existing methods for in-context learning (ICL) perform poorly for AD diagnosis due to inherent complexity of task. |
| Approach: | They propose a demonstration selection strategy that leverages a delta score to assess the relative gains of each training example and a KNN-based retriever that dynamically selects optimal “representatives” for a given input. |
| Outcome: | The proposed model outperforms existing methods on two AD detection datasets and surpasses even supervised classifiers. |
Using tournaments to calculate AUROC for zero-shot classification with LLMs (2025.findings-emnlp)
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| Challenge: | Large language models perform well on zero-shot classification tasks, but are difficult to compare to supervised classifiers due to the lack of a modifiable decision boundary. |
| Approach: | They propose a method that transforms binary classification tasks into pairwise comparisons between instances within a dataset. |
| Outcome: | The proposed method produces relative rankings of instances using the Elo rating system . |