# Quantification and Analysis of Scientific Language Variation Across Research Fields

## TL;DR

This paper introduces a neural language model-based method to quantify and analyze linguistic variation across scientific research fields, aiding interdisciplinary understanding and collaboration.

## Contribution

It presents a novel computational approach using neural embeddings to measure semantic differences of terms across research domains.

## Key findings

- The model effectively identifies terms with significant semantic change.
- It provides a metric to quantify overall linguistic variation.
- The approach improves cross-disciplinary data collaboration.

## Abstract

Quantifying differences in terminologies from various academic domains has been a longstanding problem yet to be solved. We propose a computational approach for analyzing linguistic variation among scientific research fields by capturing the semantic change of terms based on a neural language model. The model is trained on a large collection of literature in five computer science research fields, for which we obtain field-specific vector representations for key terms, and global vector representations for other words. Several quantitative approaches are introduced to identify the terms whose semantics have drastically changed, or remain unchanged across different research fields. We also propose a metric to quantify the overall linguistic variation of research fields. After quantitative evaluation on human annotated data and qualitative comparison with other methods, we show that our model can improve cross-disciplinary data collaboration by identifying terms that potentially induce confusion during interdisciplinary studies.