DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature

Abstract: In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined: DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.

• The paper presents DRIFT, a novel toolkit that integrates keyword extraction, semantic drift, and trend tracking to analyze scientific literature over time.
• It employs TF-IDF, TWEC embeddings, and LDA topic modeling to quantify term evolution and thematic changes across decades.
• Application on the cs.CL corpus reveals actionable insights, such as the rising influence of 'BERT' and the decline of 'LSTM' in computational linguistics.

Overview of the DRIFT Toolkit for Diachronic Analysis of Scientific Literature

The paper presents a toolkit named DRIFT, which is designed to facilitate diachronic analysis of scientific research corpora. DRIFT stands for DiachRonic Analysis of ScientIFic LiTerature and serves as a streamlined, user-friendly application that allows researchers to identify and track trends in scientific literature over time. This paper outlines the development, methodology, and utility of DRIFT, emphasizing its ease of use and flexibility for researchers interested in temporal linguistic analysis.

Methodological Innovations and Contributions

The authors of the study have synthesized various methodologies from well-established research works and introduced their own contributions to the field of diachronic analysis. The core capabilities of DRIFT include:

• Keyword Extraction and Word Clouds: Utilizing normalized frequency or TF-IDF algorithms, DRIFT generates visual representations of the most prominent terms in a corpus for specified periods.
• Productivity and Frequency Analysis: Inspired by existing methods, it involves clustering terms as growing, consolidated, or declining based on normalised frequency and term productivity measures.
• Semantic Drift Analysis: Temporal shift in word meanings is computed using TWEC embeddings, which align word vectors over time, allowing researchers to observe how keywords evolve semantically.
• Trend Tracking with Similarity: This involves assessing how a topic progresses through time by identifying the most similar words at defined intervals.
• LDA Topic Modeling: Employs Latent Dirichlet Allocation for discovering latent topics within a time-sliced document collection.

These methodologies are supported by the TWEC (Temporal Word Embeddings with Compass) model, which forms the backbone of the semantic analyses performed by DRIFT. TWEC's ability to efficiently manage large corpora while providing temporally consistent embeddings enhances the analytic capabilities of the toolkit.

Application and Results

The study included a case analysis focused on the Computation and Language (cs.CL) sub-domain within the arXiv repository, using abstracts from 1994 to 2021. This application demonstrated essential functionalities of DRIFT, including its dashboard with training and analysis modes, which collectively enhance the accessibility and application of diachronic analysis methods. The toolkit's design supports users from preprocessing to model training to diverse analyses, offering customizable settings for each functionality.

Numerical Results and Claims

The testing on the cs.CL corpus exhibited robust results in capturing semantic drift, productivity, and frequency dynamics. For example, the relative shift of concepts over time demonstrated by computed semantic drift is a numerical outcome that supports the theoretical understanding of such phenomena. Terms like "BERT" were identified as growing, while others like "LSTM" showed decline, reflecting realistic trends in the computational linguistics domain.

Implications and Future Directions

The direct implication of the work encapsulated in DRIFT is the provision of a holistic tool that amalgamates complex diachronic linguistic analysis into an accessible application. This facilitates the revival and exploration of dormant research topics, potentially inciting novel research directions.

The paper posits potential extensions of this work, including the integration of semi-automated inference mechanisms and modular expansions to incorporate more analysis methods. Such future developments could significantly broaden the scope and utility of DRIFT within the scientific community.

By bridging ease of use with a robust analytical framework, DRIFT presents itself as a valuable asset in diachronic research literature analysis, promoting a deeper understanding of the evolution of language within scientific domains.