基於語言模型與詞典方法的三種命名實體辨識模型架構之比較

NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach

此次任務的目的是設計一個方法標記在句子中的醫療實體詞以及它們的類別。本研究提出三種模型。第一種是以BERT模型結合線性分類器；第二種是一個兩階段模型，兩階段都是BERT模型結合分類器的次模型，但一階段只判斷句子中是否有醫療實體詞、二階段才專注於實體類別分類。第三種是結合前兩種模型以及一個基於詞典的模型，整合三個模型的結果後預測。實驗顯示這些模型在驗證與測試集的表現差異不大，最佳的模型Run 1在F1的值為0.7569。

ROCLING 2022 shared task is to design a method that can tag medical entities in sentences and then classify them into categories through an algorithm. This paper proposes three models to deal with NER issues. The first is a BERT model combined with a classifier. The second is a two-stage model, where the first stage is to use a BERT model combined with a classifier for detecting whether medical entities exist in a sentence, and the second stage focuses on classifying the entities into categories. The third approach is to combine the first two models and a model based on the lexicon approach, integrating the outputs of the three models and making predictions. The prediction results of the three models for the validation and testing datasets show little difference in the performance of the three models, with the best performance on the F1 indicator being 0.7569 for the first model.