Papers with Random Forest
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages (N18-3)
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| Challenge: | We are generating millions of titles using machine translation, but they are prone to errors. |
| Approach: | They propose a Random Forest model which explores hand-crafted features and new features . they also propose SNs which embed metadata and generated title in the same space . |
| Outcome: | The proposed models outperform the existing models on in-house data. |
Sentiment Analysis of Yelp Review Dataset: A Comparative Study of Machine Learning Methods (2026.acl-srw)
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| Challenge: | Existing methods for sentiment analysis are inconsistent and require manual processing. |
| Approach: | They use natural language processing and machine learning to classify Yelp reviews' sentiments. |
| Outcome: | The proposed model outperforms other models on Yelp reviews. |
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. |
LLM4Vis: Explainable Visualization Recommendation using ChatGPT (2023.emnlp-industry)
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| Challenge: | Existing methods to perform visualization recommendation require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results. |
| Approach: | They propose a new method that uses a ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples. |
| Outcome: | The proposed method outperforms or performs similarly to supervised learning models like Random Forest, Decision Tree, and MLP, in both few-shot and zero-shot settings. |
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications (2024.naacl-long)
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| Challenge: | Recent studies suggest using large language models to make tabular classifications . however, LLMs have been shown to exhibit harmful social biases based on stereotypes and inequalities present in society. |
| Approach: | They propose to use large language models to make tabular classifications . they show that LLMs inherit biases from their training data . |
| Outcome: | The proposed models exhibit harmful biases that reflect stereotypes and inequalities in society. |
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation (L18-1)
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| Challenge: | The production of speech corpora typically involves manual labor to verify and correct the output of automatic transcription/segmentation processes. |
| Approach: | They propose to use Support Vector Machine/Support Vector Regression and Random Forest to predict transcription errors in an annotated speech corpus. |
| Outcome: | The proposed methods can be implemented as free-to-use common language and resources and technology infrastucture web services. |
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation (2025.findings-acl)
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| Challenge: | Existing frameworks for missing data imputation are lacking in a finetuning-free process and mitigating biases and uncertainty in LLM outputs. |
| Approach: | They propose a framework for imputation of large language models with a forest of few-shot learning LLM "trees" they use bipartite information graphs to identify relevant neighboring entries with feature and value granularity. |
| Outcome: | The proposed framework is based on a concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity. |
Offensive Video Detection: Dataset and Baseline Results (2020.lrec-1)
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| Challenge: | a large number of social media platforms discourage users from publishing offensive content . however, there is no method to detect offensive content on these platforms due to the high volume of publications. |
| Approach: | They propose to use text-based machine learning to detect offensive content on different platforms . they use word embedding with Deep Learning classifiers to perform best results . |
| Outcome: | The proposed methods outperform Classic and Deep Learning classifiers in Portuguese and CNN architectures in other features. |
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models (2024.findings-acl)
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| Challenge: | Existing studies show that fine-tuned textual transformer models are vulnerable to adversarial text perturbations. |
| Approach: | They extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict adversarial robustness of the fine- tuned models. |
| Outcome: | The proposed framework can be used as an additional tool for robustness evaluation since it saves 30x-193x runtime compared to the traditional technique and can be easily used under adversarial training. |
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features (2021.emnlp-main)
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| Challenge: | ML models with handcrafted features are linguistically explainable, expandable, and competent against the modern neural models. |
| Approach: | They propose to combine traditional ML models with ML transformers to improve readability assessment by 99% accuracy. |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) accuracy on popular datasets. |
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech (2024.lrec-main)
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| Challenge: | Existing biomarkers for AD diagnosis can only be applied to relatively small sample sizes due to limited availability, excessive costs and invasive nature. |
| Approach: | They compare automatic speech recognition systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. |
| Outcome: | The proposed method improves classification performance by replacing manual transcriptions with ASR output. |