Named Entity Recognition (NER) is a sub-task of Natural Language Processing
(NLP) that distinguishes entities from unorganized text into predefined
categorization. In recent years, a lot of Bangla NLP subtasks have received
quite a lot of attention; but Named Entity Recognition in Bangla still lags
behind. In this research, we explored the existing state of research in Bangla
Named Entity Recognition. We tried to figure out the limitations that current
techniques and datasets face, and we would like to address these limitations in
our research. Additionally, We developed a Gazetteer that has the ability to
significantly boost the performance of NER. We also proposed a new NER solution
by taking advantage of state-of-the-art NLP tools that outperform conventional
techniques.

本研究探索了孟加拉命名实体识别领域的现有研究状况，找出了当前技术和数据集所面临的限制，并提出通过使用新颖的 Gazetteer 和先进的自然语言处理工具来改善命名实体识别的性能。

利用 Gazetteer 增强的 BanglaBERT 语义嵌入 K-Means 注入 CRF 模型的孟加拉命名实体识别

Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT  Semantic Embeddings K-Means-Infused CRF Model

This paper describes the system developed by the USTC-NELSLIP team for
SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER
II). A method named Statistical Construction and Dual Adaptation of Gazetteer
(SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a
statistics-based approach to construct a gazetteer. Secondly, the
representations of gazetteer networks and language models are adapted by
minimizing the KL divergence between them at both the sentence-level and
entity-level. Finally, these two networks are then integrated for supervised
named entity recognition (NER) training. The proposed method is applied to
XLM-R with a gazetteer built from Wikidata, and shows great generalization
ability across different tracks. Experimental results and detailed analysis
verify the effectiveness of the proposed method. The official results show that
our system ranked 1st on one track (Hindi) in this task.

本文提出了一种名为 SCDAG 的 SCDAG 方法用于多语言复杂命名实体识别，该方法首先使用基于统计的方法构建词表，然后通过最小化句子级别和实体级别之间 KL 散度的适应方式来适应词表网络和语言模型的表示，并将这两个网络集成用于监督命名实体识别的训练。实验结果和详细分析验证了所提出方法的有效性，并在任务中的一个路线（印地语）上排名第一。

USTC-NELSLIP 在 SemEval-2023 任务 2 中的表现：针对多语言复杂 NER 的地名词典的统计构建和双重适应

USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual  Adaptation of Gazetteer for Multilingual Complex NER

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance
span boundary detection and type classification. However, to further understand
the generalizability and effectiveness of gazetteers, the NLP community still
lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper,
we first re-examine the effectiveness several common practices of the
gazetteer-enhanced NER models and carry out a series of detailed analysis to
evaluate the relationship between the model performance and the gazetteer
characteristics, which can guide us to build a more suitable gazetteer. The
findings of this paper are as follows: (1) the gazetteer improves most of the
situations that the traditional NER model datasets are difficult to learn. (2)
the performance of model greatly benefits from the high-quality pre-trained
lexeme embeddings. (3) a good gazetteer should cover more entities that can be
matched in both the training set and testing set.

本文通过重新检验增强命名实体识别的常见做法和详细分析模型性能和词表特征之间的关系，指导我们构建更合适的词表。发现：（1）词表有助于解决传统 NER 模型难以学习的情况。（2）模型性能极大地受益于高质量的预训练词汇嵌入。（3）一个好的词表应该涵盖更多在训练集和测试集中都能匹配到的实体。