hep-th (1807.00735v1)

Abstract: 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.

• The paper demonstrates that applying Word2Vec to a vast corpus of high-energy physics literature uncovers both semantic and syntactic relationships.
• The study employs CBOW and Skip-Gram models alongside an SVM classifier, achieving up to 87.1% accuracy in binary classification tasks.
• The analysis enhances automated document categorization and reveals distinct linguistic signatures and sociological dynamics within physics subfields.

Analysis of Linguistic Structures and Automated Classification in High-Energy Physics Literature

This paper undertakes a detailed investigation into the linguistic structures and classification of theoretical high-energy physics literature by applying advanced techniques from NLP, computational linguistics, and machine learning. The authors focus primarily on the titles and abstracts of papers from the arXiv repository, specifically in the fields of hep-th (high-energy physics - theory) and related sections: hep-ph, hep-lat, gr-qc, and math-ph.

The core of the research involves extracting the text from these documents, totaling around 120,000 titles and 608,000 sentences from abstracts, to generate a corpus suitable for machine learning analysis. The Word2Vec model, a prominent NLP tool, is employed to transform these textual data into vector representations in a high-dimensional space. By doing so, the paper aims to uncover the syntactic and semantic relationships within the language of theoretical physics, as well as to facilitate the automatic classification of these documents into their respective fields.

Key Findings and Methodology

1. Textual Data Processing: The data preprocessing stage involves cleaning the text by converting it to lower-case, removing punctuation, and handling domain-specific terms and acronyms. This step is crucial for reducing noise and ensuring that the machine learning algorithms operate on the most relevant textual features.
2. Use of Word2Vec: The Word2Vec model experiments with two architectures, Continuous Bag of Words (CBOW) and Skip-Gram. By mapping words to a vector space, the model captures semantic relationships between words, such as similarity and analogy, which are crucial for understanding the language of high-energy physics.
3. Syntactic Identities and Semantic Analysis: The paper establishes syntactic identities within the corpus, showcasing examples such as "holography + quantum + string + ads = extremal-black-hole". These findings highlight the capability of the Word2Vec model to implicitly learn and reveal relationships between complex scientific concepts.
4. Classification and Confusion Matrices: A Support Vector Machine (SVM) classifier is trained on the word vectors to categorize the papers into their respective arXiv sections. The classification accuracy reaches 65.1% in five-fold classification and 87.1% for the binary classification of formal versus phenomenological sections. This demonstrates the potential of NLP and machine learning methodologies in automated document classification for specialized scientific domains.
5. Sociological Implications: The paper's analysis provides insights into the sociological dynamics of the physics community by examining language use patterns. The results suggest that although authors from various subfields share vocabulary, their contextual use differs significantly, contributing to distinct linguistic signatures in arXiv sections.
6. Future Applications and Impact: The success of this research indicates future potential for deploying such computational techniques in streamlining information retrieval within scientific databases, aiding researchers in staying current with developments across fields. Enhanced understanding and classification of textual data can also facilitate interdisciplinary collaboration by revealing previously unnoticed connections between research areas.

Conclusion

This paper exemplifies the intersection of computational linguistics and physical sciences by applying machine learning to unravel and classify the language inherent in high-energy physics literature. The implications extend beyond mere classification to understanding the inherent sociological structures and dynamics within scientific communities, fostering a more interconnected and efficient scientific discourse. The methodologies employed could serve as a framework for similar analyses across other domains, advancing the development of intelligent systems in managing the ever-growing corpus of scientific knowledge.