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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “scientometrics”

Using Supervised Learning to Classify Metadata of Research Data by Discipline of Researchcs.IR

The study uses supervised learning to classify research data by discipline.

problem Automatically categorizing research data by discipline for scientometric analysis.

method Used a large dataset of 609,524 records for training and evaluation, employing tree-based models and neural networks.

result Multi-layer perceptron models outperformed Long Short-Term Memory models in multi-label classification tasks.

supervised learning metadata classification research data discipline of research scientometrics

2019-10-16abs ↗pdf ↗

SentiCite: An Approach for Publication Sentiment Analysiscs.CL

SentiCite analyzes citations for sentiment and nature, improving on existing methods.

problem Identifying quality scientific work amidst many citations.

method Sentiment analysis of citations with motivation detection.

result SentiCite outperforms state-of-the-art methods with a F1-measure of 0.71.

sentiment analysis citation analysis scientific publications sentiment detection citation motivation

2019-10-07abs ↗pdf ↗

Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Methodcs.CY

Social media enhances or diminishes scientific status, depending on usage.

problem Impact of social media on scientific stratification and mobility.

method Logistic Attribution Analysis combining statistical and machine learning methods.

result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.

social media scientific stratification mobility logistic attribution machine learning

2024-07-23abs ↗pdf ↗

Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patentscs.LG

Paper introduces ML for rare-event prediction in patent quality estimation.

problem Lack of predictive modeling in econ, management, tech forecasting.

method Introduces ML approach for optimizing predictive performance.

result Demonstrates synergy between ML and inferential statistics.

Predictive modeling Machine learning Patent quality Rare events Classification trees

2020-03-30abs ↗pdf ↗