Identifying Scientific Names in Biodiversity Literature at New York Botanical Garden

by Rosamond Thalken

The Challenge

Biodiversity literature is dedicated to the identification, documentation, and categorization of plants, fungi, animals, and other living organisms. Correctly extracting the name of an organism within these documents involves finding the entire scientific name–including the genus, specific epithet, and author name (a plant name shown below). Extracting these names allows biologists to access documents about a species more comprehensively, and to track an organism’s history of documentation by botanists, which includes biological changes and changes in how scientists describe them. However, correctly finding organisms by their scientific names is made difficult by ambiguous abbreviations, changing botanical and zoological codes and conventions, and poor data quality.

The Discovery and Exploration Process 

During my time as a Siegel Family Endowment PiTech PhD Impact Fellow, I partnered with the New York Botanical Gardens, and specifically Damon Little and Nelson Salinas, to employ deep learning and context-based language modelling to create a system that can extract scientific plants in biodiversity literature. This is the second version of an existing project, created by Little, that can be accessed through a web interface: Quaesitor. In this new stage of the project, using a large language model allows for the language around a scientific name, such as location or descriptor, to be used as an informative clue as to what the name might be. This project culminated in a generative language model for scientific names. This model takes a selection of text from biodiversity literature as input and outputs a sequence of text in the form of a scientific name. Ideally, this model returns three pieces of information: 1) the genus, 2) the specific epithet, and 3) the author (the person who first described the plant in the scientific literature). 

You can access the model, t5-base-sci-names, at its HuggingFace repository. You can also play with the fine-tuned model or review the fine-tuning code.

An example of the T5 model’s text generation

A Multilingual botanical language model

The botanical and zoological communities have their own changing norms, codes, and goals. Moreover, even though they are specialized research communities, their literature exists across centuries and in many languages, requiring the model to be multilingual. In biodiversity literature, even a single document often shifts between languages. For example, until 2012, it was a standardized convention that every newly discovered plant would be—at least in part—described in Botanical Latin (a language designed for describing plants, with only relative similarity to Classical Latin). There is also a range of types of documents, from checklists to revisions, introductions, etc., all of which have text structured in unique ways. For these reasons, a domain-agnostic and generalizable language model, which is often trained on Wikipedia entries or books, is a poor fit for the type of documents it will analyze in practice. Therefore, this project requires domain adaptation, a process of training an existing language model to better fit the specific domain. 

As a first step, I fine-tuned a T5-Base text-generative model to the task at hand: predicting scientific names. During fine-tuning, the model was given hand-annotated examples of input and output texts, to teach it to learn to output an expanded scientific name, when possible. Text generation models can be improved through simple changes to the structure of prompts and output formatting, so I compared the accuracy of the generated text when given a clearer structure, like “genus = [genus_name]” and “author = [author_name]”. 

Qualitative Evaluation

The formatted output demonstrates whether the model has located a term as genus, epithet, or author, and tends to have more expanded abbreviations (e.g. “C. impar” becomes “Chysops impar”). Many of the formatted examples only output a few names, whereas the unformatted output is more likely to list a longer set of names. However, the unformatted output is more likely to repeat the same scientific name multiple times, to attempt to mimic examples it received during training. 

Quantitative Evaluation

I evaluated the degree of differences, information retained, and information added through a combination of string matching with Levenshtein distance and common classification measurements: precision, recall, and F1 (shown below). If the document only includes a single scientific name, the model performs best with formatted output. If the document lists more than 2-3 names, the model retains the most information without additional formatting. Precision is consistently highest with formatted output, but information is often lost (harming recall) when outputting formatted text.

Path Forward

While this performance is already relatively high, especially when a document has a single scientific name, there are steps toward improving its performance. Primarily, we could take a step back in the language-model training pipeline and continue pretraining (before fine-tuning) on biodiversity literature to create a “biological” language model–a model of how language operates in biodiversity documents. I prepared the groundwork for continued pretraining, as I collected a large dataset from the Biodiversity Heritage Library (BHL). The BHL’s data has a range of optical character recognition (OCR) quality, so I designed multiple tests for assessing the OCR quality of a given document. The resulting dataset includes 13 languages (Czech, English, French, German, Italian, Japanese, Latin, Norwegian, Polish, Portuguese, Russian, Spanish, and Swedish) and spans multiple centuries. This BHL dataset and OCR detection code will be released as an accompanying part of this project. 

Rosamond Thalken

Impact

The fine-tuned language model can be incorporated into systems to aid with the identification and search indexing of archives about plants and animals. This project is an initial exploration into how advancements in text generation can enable biologists to study biodiversity, in context.

Citations

Little, D.P. 2020. Recognition of Latin scientific names using artificial neural networks. Applications in Plant Sciences 8(7): e11378.