Automatically Cataloging Scholarly Articles using Library of Congress Subject Headings (2021)

Abstract: Institutes are required to catalog their articles with proper subject headings so that the users can easily retrieve relevant articles from the institutional repositories. However, due to the rate of proliferation of the number of articles in these repositories, it is becoming a challenge to manually catalog the newly added articles at the same pace. To address this challenge, we explore the feasibility of automatically annotating articles with Library of Congress Subject Headings (LCSH). We first use web scraping to extract keywords for a collection of articles from the Repository Analytics and Metrics Portal (RAMP). Then, we map these keywords to LCSH names for developing a gold-standard dataset. As a case study, using the subset of Biology-related LCSH concepts, we develop predictive models by formulating this task as a multi-label classification problem. Our experimental results demonstrate the viability of this approach for predicting LCSH for scholarly articles.

## introduction an institutional repository (ir) is the collection of scholarly work hosted and maintained by institutions such as universities. for example, "scholar-works foot_0 is an open access repository for the capture of the intellectual work of montana state university (msu) in support of its teaching and research goals". repository analytics and metrics portal (ramp) is a web service that accurately counts item downloads for each article in the institutional repository @xcite . besides counting the number of downloads, ramp stores metadata of the articles such as title, abstract, and keywords. currently, nearly 40 institutions have registered their repositories with ramp. to facilitate the easy finding of articles, the ir managers need to catalog them using different subject headings manually. one of the most popular vocabularies for cataloging is the library of congress subject headings (lcsh) @xcite . lcsh is a subject indexing language that is actively maintained since 1898 to catalog materials in the library of congress and most widely adopted by large and small libraries around the world @xcite . a subject heading is the most specific word or a group of words that capture the essence of a subject category. due to the rapid growth of items in irs, manual cataloging using lcsh or other vocabularies is becoming highly resourceconsuming @xcite . due to the above challenge, there have been a few previous attempts on the automatic assignment of lcsh through keyword extraction @xcite , by collecting lcsh concepts that are assigned to similar texts @xcite , using semantic similarity @xcite , and co-occurrence-based mapping @xcite . these techniques primarily depend on the presence of the keywords or similar words/ phrases within the actual text and do not utilize machine learning. furthermore, one of the studies claims that the prediction of lcsh using machine learning may be infeasible due to the large size of the vocabulary leading to inadequate training data @xcite . note that machine learning has been used for a seemingly similar but actually different task of predicting library of congress classification (lcc) @xcite . however, despite the similarity in their names, lcc and lcsh are completely different vocabularies. semantic indexing with other vocabularies has gained traction recently @xcite @xcite . most notably, predicting medical subject headings (mesh) for biomedical literature using machine learning and deep learning techniques has seen significant recent interest @xcite @xcite @xcite thanks to the bioasq challenge on biomedical semantic indexing @xcite . in this work, we explore the feasibility of developing an automated pipeline for predicting lcsh for scholarly articles using machine learning. as a case study, we leverage an extensive collection of scholarly articles from ramp and generate a gold-standard dataset by assigning biology-related lcsh concepts to each article through web scraping and string matching techniques. using this gold-standard data, we develop predictive models that can predict lcsh by modeling this as a multi-label classification problem. our experimental results indicate the effectiveness of the proposed approach. ## results and discussion figure 2 shows variation of performance of all three against the frequency. the subjects between range @xcite are widely spread across the yaxis (f max ) for each model, which indicates that the easiest and the hardest subject to predict have similar subject frequencies. top ten easiest and hardest subjects across all three models are listed in we also assess the performance of each model per document type, as reported in ## conclusions and future work in this work, we explore the feasibility of using machine learning for predicting lcsh for scholarly articles. we first generate a gold-standard dataset annotated with lcsh subjects by web scraping/ string matching and utilize this data for developing multi-label classification models. our results indicate the feasibility of our approach. we believe our approach is applicable to other data similar to lcsh concepts. this automated pipeline should be extremely valuable to librarians for expediting the manual cataloging process. we plan to measure the efficiency gains of this method through the montana state university library. while our approach displays promising results, there are many different avenues for future investigation. first, in this work, we map the web scraped keywords to subject names (instead of identifiers or ids). however, some subject names may map to more than one identifier (e.g., psychology: sh85108459 or sh2002011487). so, we plan to explore two different solutions to this. one approach is to develop a chain-classifier that can predict the lcsh ids using the already predicted subjects (i.e., a second classifier for disambiguation). another option is the improve the web scraping/ string matching pipeline so that we can generate a goldstandard dataset directly annotated with ids. to improve the performance of our traditional machine learning models, we plan to investigate the inclusion of hand-engineered features, other resources such as mesh terms, metadata fields that were ignored in this study, and the hierarchical information from the lcsh. besides, using larger more sophisticated language models (e.g., megatron-lm), using the complete set of lcsh terms (without restricting to biology-related), and structured output models that explicitly use the hierarchy information will likely improve performance. moreover, extreme multi-label (xml) models that are equipped to handle very large sets of classes @xcite will also likely provide better performance.