Quantification and Analysis of Scientific Language Variation Across Research Fields
This work addresses the challenge of terminology differences for researchers in interdisciplinary studies, though it is incremental as it builds on existing methods for semantic analysis.
The paper tackled the problem of quantifying linguistic variation across scientific research fields by proposing a computational approach using a neural language model to analyze semantic changes of terms, and showed that it can improve cross-disciplinary data collaboration by identifying confusing terms.
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.