DRIFT : A Toolkit for Diachronic Analysis of Scientific Literature (2021)

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 .

## introduction historians perform comparative studies between the past and the present to explain certain phenomena. studying the past also helps us in making plausible predictions about the future. major sources of information about the past are old-age texts, tomes and manuscripts. language changes and shifts with changes in society and culture. for example, words such as curglaff and lunting have become obsolete, the word gay's meaning has changed entirely and new words such as lol and rofl have gained prominence with the emergence of the "texting generation". in the research world, analysis of old articles and papers is gaining importance. analysing old documents can revive research topics which have been forgotten over the decades; removing the cob- * equal contribution. author ordering determined by coin flip. in this paper, we introduce a hassle-free application for the diachronic analysis of research corpora. we name our application drift, an acronym for diachronic analysis of scientific literature. drift provides researchers a one-click way to perform various diachronic analyses. our contribution is two-fold: 1) we assemble (and propose) different methods for diachronic analysis of research corpora; 2) we make it extremely convenient for researchers to analyse research trends; this, in turn, will encourage researchers to find latent, quiescent topics which may have huge research potential in the near future. the main principles behind the design for drift are ease of usage and flexibility. the rest of the paper is organised as follows. in section 2, we perform an extensive literature survey. section 3 describes the research corpora and the methods for crawling the corpora. in section 4, we explain the training methodology for obtaining diachronic embeddings and our easy-to-use application's dashboard and layout in detail. section 5 elucidates the various analysis methods which researchers can employ with a mere click of a button. ## accessibility the code for the toolkit is open-sourced, and is distributed under a mit license. the latest stable code release is available here. an online demo of the application is hosted here. a set of short video demonstrations can be found here. ## conclusion and future work in this paper, we present drift, a hassle-free, userfriendly application for diachronic analysis of scientific literature. we compile well-cited research works and provide an interactive and intuitive userinterface using streamlit. we perform a case-study on cs.cl corpus from arxiv repository, and demonstrate the effectiveness and utility of our application in analysing such corpora. as an extension of our work, apart from adding upcoming analysis methods (citation-based/statistical/knowledge graphs), we also intend to make our application modular to make it easier for users to add their own methods, provide semi-automated inference from the graphs/tables, and allow easy integration with l a t e x. anne-kathrin schumann. 2016. brave new world: uncovering topical dynamics in the acl anthology reference corpus using term life cycle information. in proceedings of the 10th sighum workshop on language technology for cultural heritage, social sciences, and humanities, pages 1-11, berlin, germany. association for computational linguistics. terrence szymanski. 2017. temporal word analogies: identifying lexical replacement with diachronic word embeddings. in proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers), pages 448-453, vancouver, canada. association for computational linguistics. zhilin yang, zihang dai, yiming yang, jaime carbonell, russ r salakhutdinov, and quoc v le. 2019. xlnet: generalized autoregressive pretraining for language understanding. in advances in neural information processing systems, volume 32. curran associates, inc. a algorithms a.1 semantic shift an overview of the approach is given in algorithm 1. the dist(x, y) function can either be the euclidean distance or cosine distance. in the algorithm, we find the most drifted words from a given list of words by sorting them according to the distance. year_x_model is the aligned twec model for year x. algorithm 1: semantic shift data: words, year_1, year_2 result: word_dist word_dist ← {} for word in words do year_1_emb ← year_1_model(word) year_2_emb ← year_2_model(word) word_dist[word] ← dist(year_1_emb, year_2_emb) end word_dist ← sort_by_value(word_dist, "desc")