生物醫學實體檢測模型之實驗與錯誤分析

SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model

生物醫學之命名實體辨識相較於一般命名實體辨識任務來得更加複雜。本次命名實體辨識任務以辨識醫療保健領域的十種命名實體類型為目的，預測句子的命名實體邊界和類別。我們探討了命名實體辨識的多種基礎方法，如隨機森林、隱馬爾可夫模型、條件隨機場和BERT。提供未來在醫療領域的NER辨識中，能選擇最佳表現的基礎方法為基準進行改良。預測結果以BERT模型在F-score上較為顯著，取得了更好的結果。

Named entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical field, it may be drug names, Organ names, test items, nutritional supplements, etc. The purpose of named entity recognition in this study is to search for the above items from unstructured input text. In this study, taking healthcare as the research purpose, and predicting named entity boundaries and categories of sentences based on ten entity types, We explore multiple fundamental NER approaches to solve this task, Include: Hidden Markov Models 、 Conditional Random Fields、Random Forest Classifier and BERT. The prediction results are more significant in the F-score of the CRF model, and have achieved better results.