TaxoNERD: Deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature

Abstract

Easy access to multi‐taxa information (e.g. distribution, traits, diet) in the scientific literature is essential to understand, map and predict all‐inclusive biodiversity. Tools are needed to automatically extract useful information from the ever‐growing corpus of ecological texts and feed this information to open data repositories. A prerequisite is the ability to recognise mentions of taxa in text, a special case of named entity recognition (NER). In recent years, deep learning‐based NER systems have become ubiquitous, yielding state‐of‐the‐art results in the general and biomedical domains. However, no such tool is available to ecologists wishing to extract information from the biodiversity literature. We propose a new tool called TaxoNERD that provides deep neural network (DNN) models to recognise taxon mentions in ecological documents. To achieve high performance, these models usually need to be trained on a large corpus of manually annotated text. Creating such a corpus is a laborious and costly process, with the result that manually annotated corpora in the ecological domain tend to be too small to learn an accurate DNN model from scratch. To address this issue, we leverage existing models pretrained on large biomedical corpora using transfer learning. The performance of our models is evaluated on four corpora and compared to the most popular taxonomic entity recognition tools. Our experiments suggest that existing taxonomic NER tools are not suited to the extraction of ecological information from text as they performed poorly on ecologically oriented corpora, either because they do not take account of the variability of taxon naming practices or because they do not generalise well to the ecological domain. Conversely, a domain‐specific DNN‐based tool like TaxoNERD outperformed the other approaches on an ecological information extraction task. Efforts are needed to raise ecological information extraction to the same level of performance as its biomedical counterpart. One promising direction is to leverage the huge corpus of unlabelled ecological texts to learn a language representation model that could benefit downstream tasks. These efforts could be highly beneficial to ecologists on the long term.

Machine learning and deep learning—A review for ecologists

“…In ecology, for example, RNNs have been used to predict population dynamics (Joseph, 2020) or animal movements (Rew et al, 2019; see Borowiec et al, 2022 for more details on different DL algorithms). DL algorithms have also been used to synthesize taxonomic information from literature (Le Guillarme & Thuiller, 2022) or to predict species interactions in ecological networks (Strydom et al, 2021), or to predict species distributions (Deneu et al, 2021). In the following, we treat DL as a subfield of ML and only mention DL when relevant differences to classical ML algorithms are involved.…”

A critical analysis of plant science literature reveals ongoing inequities

“…The species studied in each paper were identified from abstracts using the Python package TaxoNERD (53). Each biological entity was assigned to a NCBI taxonomy ID and higher-level taxonomic classifications were extracted with ETE Toolkit (54).…”

An NLP framework for extracting causes, consequences, and hazards from occurrence reports to validate a HAZOP study

“…The operation of spaCy can be broken down into a four step process (see figure 2); initially terms are embedded via a bloom filter into a continuous vector space [18]. Next, a convolutional neural network is used to encode the terms into General neural architecture for spaCy NER [20] a sentence matrix [19] therefore taking context into account.…”