BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain

Abdelmageed, Nora; Löffler, Felicitas; Feddoul, Leila; Algergawy, Alsayed; Samuel, Sheeba; Gaikwad, Jitendra; Kazem, Anahita J. N.; König-Ries, Birgitta

doi:10.3897/bdj.10.e89481

Cited by 4 publications

(8 citation statements)

References 21 publications

(33 reference statements)

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“…It consists of abstract-length documents: 250 from BHL, 150 from journal articles, and 100 from government reports. The most recent addition to the list of biodiversity corpora is BiodivNERE (Abdelmageed et al, 2022 ). Drawn from biodiversity dataset metadata and abstracts, it comes with two datasets, one supporting NER and the other supporting Relation Extraction (RE).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species

Gabud,

Lapitan,

Mariano

et al. 2024

Front. Artif. Intell.

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IntroductionFine-grained, descriptive information on habitats and reproductive conditions of plant species are crucial in forest restoration and rehabilitation efforts. Precise timing of fruit collection and knowledge of species' habitat preferences and reproductive status are necessary especially for tropical plant species that have short-lived recalcitrant seeds, and those that exhibit complex reproductive patterns, e.g., species with supra-annual mass flowering events that may occur in irregular intervals. Understanding plant regeneration in the way of planning for effective reforestation can be aided by providing access to structured information, e.g., in knowledge bases, that spans years if not decades as well as covering a wide range of geographic locations. The content of such a resource can be enriched with literature-derived information on species' time-sensitive reproductive conditions and location-specific habitats.MethodsWe sought to develop unsupervised approaches to extract relationships pertaining to habitats and their locations, and reproductive conditions of plant species and corresponding temporal information. Firstly, we handcrafted rules for a traditional rule-based pattern matching approach. We then developed a relation extraction approach building upon transformer models, i.e., the Text-to-Text Transfer Transformer (T5), casting the relation extraction problem as a question answering and natural language inference task. We then propose a novel unsupervised hybrid approach that combines our rule-based and transformer-based approaches.ResultsEvaluation of our hybrid approach on an annotated corpus of biodiversity-focused documents demonstrated an improvement of up to 15 percentage points in recall and best performance over solely rule-based and transformer-based methods with F1-scores ranging from 89.61 to 96.75% for reproductive condition - temporal expression relations, and ranging from 85.39% to 89.90% for habitat - geographic location relations. Our work shows that even without training models on any domain-specific labeled dataset, we are able to extract relationships between biodiversity concepts from literature with satisfactory performance.

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Section: Related Workmentioning

confidence: 99%

Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species

Gabud,

Lapitan,

Mariano

et al. 2024

Front. Artif. Intell.

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“…Some models have been pre-trained using scientific corpora such as SciBERT, trained on a random sample of 1. Despite their demonstrated use in biomedical sciences, large language models are just beginning to be adopted in ecology and evolution [1,15,53,54], and to our knowledge there is currently only one large language model, BiodivBERT, trained explicitly on biodiversity-related texts [53].…”

Section: -Language Models: Deep Learning In Nlpmentioning

confidence: 99%

“…Despite many promising applications of NLP in ecology [1,18], we currently have few domain-specific NLP tools [15,53]. This is in part because ecology is a low-resource domain [57], meaning there are few large open-access text databases available for training foundation models [1], and there are few gold standard databases with task-specific labels needed for supervised learning.…”

Section: State-of-the-art In Ecology and Evolution: Learning In A Low...mentioning

confidence: 99%

“…This is in part because ecology is a low-resource domain [57], meaning there are few large open-access text databases available for training foundation models [1], and there are few gold standard databases with task-specific labels needed for supervised learning. Some ecology-specific gold standard datasets exist, such as COPIOUS, a corpus directed towards the extraction of species occurrences from the biodiversity literature [58], and the newer BiodivNERE [59], a gold standard corpus for NER and RE. While gold standard datasets are essential for comparing the performance of new approaches as they are developed, they do not currently provide enough examples to train deep learning NLP models from scratch.…”

Section: State-of-the-art In Ecology and Evolution: Learning In A Low...mentioning

confidence: 99%

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The changing landscape of text mining - a review of approaches for ecology and evolution

Farrell,

Le Guillarme,

Brierley

et al. 2024

Preprint

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In ecology and evolutionary biology, synthesis and modelling of data from published literature is a common practice for generating insight and testing theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase efficiency and dramatically improve the transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing due to developments in machine learning-based language models. Here we review the growing landscape of approaches, mapping them onto three broad paradigms (Frequency-based approaches, Traditional Natural Language Processing, and Deep learning-based language models). This review serves as an entry point to learn foundational and cutting edge concepts, vocabularies, and methods, and foster better integration of these tools into ecological and evolutionary research. We discuss approaches for modelling ecological texts, generating training data, developing custom models, and interacting with Large Language Models, and we present challenges and possible solutions to implementing these methods in ecology and evolution.

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“…Previous approaches in ecology have focused heavily on named entity recognition of species and taxonomy (Akella et al., 2012; Gerner et al., 2010; Le Guillarme & Thuiller, 2022; Millard et al., 2020) as well as geographical locations or population trends (Cornford et al., 2022) but also include document classification (Cornford et al., 2021) and relation extraction (Kaur et al., 2019). Furthermore, various gold standard databases of species names and taxonomy have been published to aid the evaluation of NER approaches in ecology (Abdelmageed et al., 2022; Nguyen et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Large language models help facilitate the automated synthesis of information on potential pest controllers

Scheepens,

Millard,

Farrell

et al. 2024

Methods Ecol Evol

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The body of ecological literature, which informs much of our knowledge of the global loss of biodiversity, has been experiencing rapid growth in recent decades. The increasing difficulty of synthesising this literature manually has simultaneously resulted in a growing demand for automated text mining methods. Within the domain of deep learning, large language models (LLMs) have been the subject of considerable attention in recent years due to great leaps in progress and a wide range of potential applications; however, quantitative investigation into their potential in ecology has so far been lacking. In this work, we analyse the ability of GPT‐4 to extract information about invertebrate pests and pest controllers from abstracts of articles on biological pest control, using a bespoke, zero‐shot prompt. Our results show that the performance of GPT‐4 is highly competitive with other state‐of‐the‐art tools used for taxonomic named entity recognition and geographic location extraction tasks. On a held‐out test set, we show that species and geographic locations are extracted with F1‐scores of 99.8% and 95.3%, respectively, and highlight that the model can effectively distinguish between ecological roles of interest such as predators, parasitoids and pests. Moreover, we demonstrate the model's ability to effectively extract and predict taxonomic information across various taxonomic ranks. However, we do report a small number of cases of fabricated information (confabulations). Due to a lack of specialised, pre‐trained ecological language models, general‐purpose LLMs may provide a promising way forward in ecology. Combined with tailored prompt engineering, such models can be employed for a wide range of text mining tasks in ecology, with the potential to greatly reduce time spent on manual screening and labelling of the literature.

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BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain

Cited by 4 publications

References 21 publications

Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species

Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species

The changing landscape of text mining - a review of approaches for ecology and evolution

Large language models help facilitate the automated synthesis of information on potential pest controllers

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