B angla B io M ed: A Biomedical Named-Entity Annotated Corpus for B angla ( B engali) (2022)

Abstract: Recognizing biomedical entities in the text has significance in biomedical and health science research, as it benefits myriad downstream tasks, including entity linking, relation extraction, or entity resolution. While English and a few other widely used languages enjoy ample resources for automatic biomedical entity recognition, it is not the case for Bangla, a low-resource language. On that account, in this paper, we introduce BanglaBioMed, a Bangla biomedical named entity (NE) annotated dataset in standard IOB format, the first of its kind, consisting of over 12000 tokens annotated with the biomedical entities. The corpus is created by collecting Bangla text from a list of health articles and then annotated with four distinct types of entities: Anatomy (AN), Chemical and Drugs (CD), Disease and Symptom (DS), and Medical Procedure (MP). We provide the details of the entire data collection and annotation procedure and illustrate various statistics of the created corpus. Our developed corpus is a much-needed addition to the Bangla NLP resource that will facilitate biomedical NLP research in Bangla.

## introduction the named-entity recognition (ner) frameworks aim to identify named entities (ne) mentioned in unstructured text documents and then categorize them into predefined domain-specific classes such as person or organization names, medical codes, chemical compounds, and food ingredients. diverse sets of named entity (ne) annotated datasets have been created by researchers in varied domains such as clinical domain @xcite , food domain @xcite , astronomy @xcite , biological domain @xcite . due to the essence of ne for understanding biomedical concepts such as diseases, chemicals, and proteins, a number of studies focused on building ner corpora for the biomedical domain as it can aid researchers in finding relevant concepts and speed up the process of biomedical scientific discovery. in english and a few other major languages, various ne corpora representing biomedical entities are publicly available @xcite . however, in bangla, such a biomedical ne annotated dataset does not exist as nlp research in bangla is still in infancy except in a few areas such as sentiment analysis @xcite @xcite @xcite , hate and abusive language detection @xcite @xcite . with the growing popularity of telemedicine and the availability of health and medical-related data written in bangla, developing a biomedical named entity recognition (ner) system in bangla is a pressing necessity. for developing a sophisticated ner system, it is essential to have at least a moderate amount of annotated data. in particular, the generalizability and performances of the machine learning approaches (especially the deep learning-based models) heavily rely on the quantity of available annotated data. hence, in this study, we introduce a biomedical ne dataset, the first of its kind, for the low-resource bangla. the dataset is created by retrieving biomedical and health-related textual content from a number of health articles. the text data are then tokenized and annotated with four types of entities: anatomy (an), chemical and drugs (cd), disease and symptoms (ds), and medical procedure (mp). the final corpus contains around 2000 tokens representing one of the four types of biomedical ne mentioned above and around 10000 non-entity tokens. ## related work although english and some other languages standardized entity annotated (i.e., iob format) biomedical corpora are available for the ner task, to the best of our knowledge, such resources do not exist in bangla. ## summary and conclusion in this study, we introduce a bangla biomedical named entity annotated corpus created from a number of bangla health articles. to the best of our knowledge, this is the first biomedical ne annotated corpus in bangla (bengali) in standard iob format created for biomedical text mining. we report detailed annotation guidelines and procedures of the annotation. moreover, we pro-vide the various statistics of four different types of biomedical entities: an, ds, cd, and mp, in the annotated corpus. we have made the corpus publicly available for the researchers. the future work will focus on enhancing the size of the annotated corpus and creating strong baselines for automatic ner tasks by incorporating transformerbased language models. besides, we will investigate how to leverage cross-lingual resources from other languages, such as english, to improve the performance of the ner task.