Biomedical Text Mining Task

A Biomedical Text Mining Task is a scientific text mining task that is restricted to a biomedical corpus (of biomedical documents within a biomedical literature.

• Context:
  • It can be supported by Biomedical Information Extraction, including Biomedical Entity NER (such as Protein NER).
  • It can be a Medical Text Mining Task.
  • …
• Example(s):
  • BioCreAtIvE Benchmark Task.
  • …
• Counter-Example(s):
  • Astrophysics Text Mining.
  • Computing Science Text Mining Task.
• See: Biomedical Domain, BioNLP Workshop, Biomedical Research Domain.

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References

2021

• (Lee et al., 2020) ⇒ Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. (2020). “BioBERT: A Pre-trained Biomedical Language Representation Model for Biomedical Text Mining.” Bioinformatics 36, no. 4
  • ABSTRACT: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts.

2010

• http://en.wikipedia.org/wiki/Biomedical_text_mining
  • Biomedical text mining (also known as BioNLP) refers to text mining applied to texts and literature of the biomedical and molecular biology domain. It is a rather recent research field on the edge of natural language processing, bioinformatics, medical informatics and computational linguistics.
  • There is an increasing interest in text mining and information extraction strategies applied to the biomedical and molecular biology literature due to the increasing number of electronically available publications stored in databases such as PubMed.

2008

• (Leitnera & Valencia, 2008) ⇒ Florian Leitnera, and Alfonso Valencia. (2008). “A Text-Mining Perspective on the Requirements for Electronically Annotated Abstracts.” In: 582(8).
  • NOTES: It proposes that Authors partially Annotate the Abstracts of their submitted Research Papers.
• (Seringhaus & Gerstein, 2008) ⇒ Michael Seringhaus and Mark Gerstein. (2008). “Manually Structured Digital Abstracts: A scaffold for automatic text mining.” In: FEBS Letters, 582
  • NOTES: It replies to responses to an earlier article recommending that author's provide annotated abstracts.

2006

• (Blake & Bult, 2006) ⇒ JA Blake, CJ Bult. (2006). “Beyond the Data Deluge: data integration and biontologies.” In: Journal of Biomedical Informatics

2005

• (Cohen & Hersh, 2005) ⇒ Aaron Michael Cohen, and William R. Hersh. (2005). “A Survey of Current Work in Biomedical Text Mining.” In: Briefings in Bioinformatics 2005 6(1). doi:10.1093/bib/6.1.57

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