A ni EE : A Dataset of Animal Experimental Literature for Event Extraction (2023)

Abstract: Event extraction (EE), as a crucial information extraction (IE) task, aims to identify event triggers and their associated arguments from unstructured text, subsequently classifying them into pre-defined types and roles. In the biomedical domain, EE is widely used to extract complex structures representing biological events from literature. Due to the complicated semantics and specialized domain knowledge, it is challenging to construct biomedical event extraction datasets. Additionally, most existing biomedical EE datasets primarily focus on cell experiments or the overall experimental procedures. Therefore, we introduce AniEE, an event extraction dataset concentrated on the animal experiment stage. We establish a novel animal experiment customized entity and event scheme in collaboration with domain experts. We then create an expert-annotated high-quality dataset containing discontinuous entities and nested events and evaluate our dataset on the recent outstanding NER and EE models.

## introduction scientific literature can provide a groundwork for further investigations and offer a valuable platform for turning academic achievements into tangible industrial outcomes. especially in the biomedical domain, the volume of literature grows exponentially @xcite 1 , posing challenges in locating relevant experimental key facts within unstructured text. to help researchers discover related literature that fulfills their needs, information extraction (ie) is actively utilized. across diverse ie tasks, event extraction (ee) has been spotlighted to figure 1 : each example for three event types from aniee: sampleadministration, positiveregulation, and negativeregulation extract the complex syntactic structures of biological events. each event consists of an event trigger and event arguments, as well as the relations between them. figure 1 (a) illustrates an example of three sampleadministration events, which includes an event trigger "i.p." and object, subject, schedule, and three amount arguments. additionally, figure 1 (b ) and (c) describe examples of positiveregulation and neg-ativeregulation, where"bnn27" is the cause of the events. among various biomedical literature, animal experiment-related articles are one of the most difficult texts to extract valuable information from. the experimental phases can be generally divided into three stages: within cells, animal experiments, and clinical trials. compared to cell experiments, animal research re- @xcite , cadec @xcite , genia 2011 (ge11) @xcite , and multi-level event extraction (mlee) @xcite . to the best of our knowledge, aniee is the first dataset on animal experiment literature containing both discontinuous entities (disc.entity) and nested events (nest.event). the number of documents and sentences is described in appendix despite the importance of experimental information, ee studies targeting animal experiment literature have rarely been conducted. one of the reasons is, as described in therefore, we introduce aniee, a named entity recognition and event extraction dataset focused on animal experiments. we establish a new entity and event scheme designed for the animal experiment stage in collaboration with domain experts. our dataset is annotated by biomedical professionals and applied to the latest competitive ner and ee models. as described in we sum up our contributions as follows: ## conclusion in order to enhance the efficiency and accuracy of a comprehensive review of existing animal experiment literature, we introduce aniee, an event extraction dataset designed specifically for the animal experimentation stage. the distinctiveness of our dataset can be summarized through two key points. to the best of our knowledge, our dataset represents the first event extraction dataset focusing on animal experimentation. in addition, our dataset encompasses both discontinuous named entities and nested events within texts at the document level. we anticipate that introducing our novel dataset, aniee, will contribute significantly to advancing document-level event extraction not only in the biomedical domain, but also in the natural language processing.