hep-th
This work provides insights into the linguistic and structural differences in physics sub-fields, which could aid in automated categorization and understanding of scientific literature, though it is incremental in applying existing methods to new data.
The study applied NLP and machine learning techniques to analyze titles from arXiv sections in high energy and mathematical physics, finding notable scientific and sociological differences between fields and achieving 87.1% accuracy in binary classification and 65.1% in five-fold classification.
We apply techniques in natural language processing, computational linguistics, and machine-learning to investigate papers in hep-th and four related sections of the arXiv: hep-ph, hep-lat, gr-qc, and math-ph. All of the titles of papers in each of these sections, from the inception of the arXiv until the end of 2017, are extracted and treated as a corpus which we use to train the neural network Word2Vec. A comparative study of common n-grams, linear syntactical identities, word cloud and word similarities is carried out. We find notable scientific and sociological differences between the fields. In conjunction with support vector machines, we also show that the syntactic structure of the titles in different sub-fields of high energy and mathematical physics are sufficiently different that a neural network can perform a binary classification of formal versus phenomenological sections with 87.1% accuracy, and can perform a finer five-fold classification across all sections with 65.1% accuracy.