Dataset Information

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

ABSTRACT:

Background

Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. To support the management and analysis of these data, two techniques from computer science are of interest, namely Named Entity Recognition (NER) and Relation Extraction (RE). While the former enables better content discovery and understanding, the latter fosters the analysis by detecting connections between entities and, thus, allows us to draw conclusions and answer relevant domain-specific questions. To automatically predict entities and their relations, machine/deep learning techniques could be used. The training and evaluation of those techniques require labelled corpora.

New information

In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.

SUBMITTER: Abdelmageed N

PROVIDER: S-EPMC9836593 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Abdelmageed Nora N Löffler Felicitas F Feddoul Leila L Algergawy Alsayed A Samuel Sheeba S Gaikwad Jitendra J Kazem Anahita A König-Ries Birgitta B

Biodiversity data journal 20221007

<h4>Background</h4>Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in this field. With this, a large amount of textual data (publications) and metadata (e.g. dataset description) has been generated. T ...[more]

PMID: 36761617

Similar Datasets

Project description:Background Species occurrence records are very important in the biodiversity domain. While several available corpora contain only annotations of species names or habitats and geographical locations, there is no consolidated corpus that covers all types of entities necessary for extracting species occurrence from biodiversity literature. In order to alleviate this issue, we have constructed the COPIOUS corpus-a gold standard corpus that covers a wide range of biodiversity entities. Results Two annotators manually annotated the corpus with five categories of entities, i.e. taxon names, geographical locations, habitats, temporal expressions and person names. The overall inter-annotator agreement on 200 doubly-annotated documents is approximately 81.86% F-score. Amongst the five categories, the agreement on habitat entities was the lowest, indicating that this type of entity is complex. The COPIOUS corpus consists of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities. Named entity recognisers trained on the corpus could achieve an F-score of 74.58%. Moreover, in recognising taxon names, our model performed better than two available tools in the biodiversity domain, namely the SPECIES tagger and the Global Name Recognition and Discovery. More than 1,600 binary relations of Taxon-Habitat, Taxon-Person, Taxon-Geographical locations and Taxon-Temporal expressions were identified by applying a pattern-based relation extraction system to the gold standard. Based on the extracted relations, we can produce a knowledge repository of species occurrences. Conclusion The paper describes in detail the construction of a gold standard named entity corpus for the biodiversity domain. An investigation of the performance of named entity recognition (NER) tools trained on the gold standard revealed that the corpus is sufficiently reliable and sizeable for both training and evaluation purposes. The corpus can be further used for relation extraction to locate species occurrences in literature-a useful task for monitoring species distribution and preserving the biodiversity.

Project description:BackgroundEntity relation extraction is an important task in the construction of professional knowledge graphs in the medical field. Research on entity relation extraction for academic books in the medical field has revealed that there is a great difference in the number of different entity relations, which has led to the formation of a typical unbalanced data set that is difficult to recognize but has certain research value.MethodsIn this article, we propose a new entity relation extraction method based on data augmentation. According to the distribution of individual entity relation classes in the data set, the probability of whether a text is augmented during training was calculated. In text-oriented data augmentation, different augmentation methods perform differently in different language environments. The reinforcement of learning determines which data augmentation method to use in the current language environment. This strategy was applied to the entity relation extraction of the medical professional book, Pharmacopoeia of the People's Republic of China, and different data augmentation methods (i.e., no data augmentation, traditional data augmentation, and reinforcement learning-based data augmentation) were compared under the same neural network model.ResultsThe deep-learning model using data augmentation was better than the model without data augmentation, as data augmentation significantly improved the evaluation indicators of the relation classes with low data volumes in the unbalanced data set and slightly improved the evaluation indicators of the relation classes with sufficient features and large data volumes. Additionally, the deep-learning model using reinforcement learning-based data augmentation was superior to the deep-learning model using traditional data augmentation. We found that after the application of reinforcement learning-based data augmentation, the evaluation indicators of the multiple relation classes were much better than those to which reinforcement learning-based data augmentation had not been applied.ConclusionsFor unbalanced data sets, data augmentation can effectively improve the ability of the deep-learning model to obtain data features, and reinforcement learning-based data augmentation can further enhance this ability. Our experiments confirmed the superiority of reinforcement learning-based data augmentation.

Dataset Information

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

Background

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Publications

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

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