myNER：基于双向LSTM与fastText嵌入并通过词性标注联合训练的上下文缅甸语命名实体识别
myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging
AI总结：
本文提出缅甸语词级NER语料库myNER，结合POS标注，通过CRF、BiLSTM-CRF与fastText嵌入的联合训练实验，证明上下文嵌入和词性联合训练能显著提升低资源语言NER性能。
摘要：
命名实体识别（NER）涉及识别和分类文本数据中的命名实体。尽管NER具有重要意义，但相关研究往往忽视了像缅甸语这样的低资源语言，主要原因是缺乏公开可用的标注数据集。为解决这一问题，我们引入了myNER，这是一个新颖的词级NER语料库，采用7标签标注方案，并辅以词性（POS）标注以提供额外的句法信息。在构建语料库的同时，我们对多种NER模型进行了全面评估，包括条件随机场（CRF）、双向LSTM（BiLSTM）-CRF，以及它们在不同设置下与fastText嵌入的组合。实验揭示了上下文词嵌入的有效性以及词性标注联合训练的影响，表明各模型均取得了显著的性能提升。传统CRF联合任务模型以fastText嵌入作为特征取得了最佳结果，准确率为0.9818，加权F1分数为0.9811，宏平均F1分数为0.7429。使用微调fastText嵌入的BiLSTM-CRF取得了最佳结果，准确率为0.9791，加权F1分数为0.9776，宏平均F1分数为0.7395。
英文摘要：
Named Entity Recognition (NER) involves identifying and categorizing named entities within textual data. Despite its significance, NER research has often overlooked low-resource languages like Myanmar (Burmese), primarily due to the lack of publicly available annotated datasets. To address this, we introduce myNER, a novel word-level NER corpus featuring a 7-tag annotation scheme, enriched with Part-of-Speech (POS) tagging to provide additional syntactic information. Alongside the corpus, we conduct a comprehensive evaluation of NER models, including Conditional Random Fields (CRF), Bidirectional LSTM (BiLSTM)-CRF, and their combinations with fastText embeddings in different settings. Our experiments reveal the effectiveness of contextualized word embeddings and the impact of joint training with POS tagging, demonstrating significant performance improvements across models. The traditional CRF joint-task model with fastText embeddings as a feature achieved the best result, with a 0.9818 accuracy and 0.9811 weighted F1 score with 0.7429 macro F1 score. BiLSTM-CRF with fine-tuned fastText embeddings gets the best result of 0.9791 accuracy and 0.9776 weighted F1 score with 0.7395 macro F1 score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
