为缅甸语设计了首个带词性标注的命名实体识别数据集及模型。
myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging
- 用双向LSTM与fastText嵌入联合训练，结合词性标注提升识别效果。
- 最佳模型达0.9811加权F1，较传统CRF提升显著。
- 适合低资源语言处理、自然语言处理研究者参考。
命名实体识别（NER）旨在从文本中识别并分类命名实体。尽管其重要性突出，但对缅甸语等低资源语言的研究仍被忽视，主要因缺乏公开标注数据集。为此，本文提出myNER，一个基于7类标注方案的词级别NER语料库，并附带词性（POS）标注以提供额外句法信息。同时，我们对多种模型进行了全面评估，包括条件随机场（CRF）、双向长短期记忆网络（BiLSTM）-CRF，及其与fastText嵌入在不同设置下的组合。实验表明，上下文嵌入与联合训练词性标注能显著提升性能。使用fastText作为特征的CRF联合任务模型表现最佳，准确率为0.9818，加权F1得分为0.9811，宏平均F1为0.7429；而微调fastText的BiLSTM-CRF模型取得0.9791准确率和0.9776加权F1，宏平均F1为0.7395。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。