We introduce CNER, an ensemble of capable tools for extraction of semantic
relationships between named entities in Spanish language. Built upon a
container-based architecture, CNER integrates different Named entity
recognition and relation extraction tools with a user-friendly interface that
allows users to input free text or files effortlessly, facilitating streamlined
analysis. Developed as a prototype version for the Natural Language Processing
(NLP) Group at Universidad del Valle, CNER serves as a practical educational
resource, illustrating how machine learning techniques can effectively tackle
diverse NLP tasks in Spanish. Our preliminary results reveal the promising
potential of CNER in advancing the understanding and development of NLP tools,
particularly within Spanish-language contexts.

CNER 是一个用于提取西班牙语命名实体之间语义关系的工具集合，采用基于容器的架构，集成不同的命名实体识别和关系提取工具，拥有用户友好的界面，可以轻松输入文本或文件，便于流程化分析。该工具在西班牙语自然语言处理任务中展示了机器学习技术的有效应用，初步结果显示 CNER 具有潜在的前景和推动自然语言处理工具发展的潜力。

CNER: 一个命名实体关系分类工具

CNER: A tool Classifier of Named-Entity Relationships

Named Entity Recognition (NER) is a fundamental task in natural language
processing that involves identifying and classifying named entities in text.
But much work hasn't been done for complex named entity recognition in Bangla,
despite being the seventh most spoken language globally. CNER is a more
challenging task than traditional NER as it involves identifying and
classifying complex and compound entities, which are not common in Bangla
language. In this paper, we present the winning solution of Bangla Complex
Named Entity Recognition Challenge - addressing the CNER task on BanglaCoNER
dataset using two different approaches, namely Conditional Random Fields (CRF)
and finetuning transformer based Deep Learning models such as BanglaBERT.
The dataset consisted of 15300 sentences for training and 800 sentences for
validation, in the .conll format. Exploratory Data Analysis (EDA) on the
dataset revealed that the dataset had 7 different NER tags, with notable
presence of English words, suggesting that the dataset is synthetic and likely
a product of translation.
We experimented with a variety of feature combinations including Part of
Speech (POS) tags, word suffixes, Gazetteers, and cluster information from
embeddings, while also finetuning the BanglaBERT (large) model for NER. We
found that not all linguistic patterns are immediately apparent or even
intuitive to humans, which is why Deep Learning based models has proved to be
the more effective model in NLP, including CNER task. Our fine tuned BanglaBERT
(large) model achieves an F1 Score of 0.79 on the validation set. Overall, our
study highlights the importance of Bangla Complex Named Entity Recognition,
particularly in the context of synthetic datasets. Our findings also
demonstrate the efficacy of Deep Learning models such as BanglaBERT for NER in
Bangla language.

本文介绍了在 BanglaCoNER 数据集上使用条件随机场（CRF）和微调变压器模型（如 BanglaBERT）的两种方法来解决 Bangla 复杂命名实体识别挑战的获胜解决方案。 深入探究了数据集，发现该数据集具有 7 个不同的 NER 标记，具有英语单词的明显存在，表明该数据集是合成的。同时，我们实验证明了基于深度学习的模型是自然语言处理中更有效的模型之一，特别是在 CNER 任务中。我们的 fine-tuned BanglaBERT（large）模型在验证集上实现 F1 Score 为 0.79。

BanglaCoNER：面向健壮的孟加拉语复杂命名实体识别

BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition

Chinese named entity recognition (CNER) is an important task in Chinese
natural language processing field. However, CNER is very challenging since
Chinese entity names are highly context-dependent. In addition, Chinese texts
lack delimiters to separate words, making it difficult to identify the boundary
of entities. Besides, the training data for CNER in many domains is usually
insufficient, and annotating enough training data for CNER is very expensive
and time-consuming. In this paper, we propose a neural approach for CNER.
First, we introduce a CNN-LSTM-CRF neural architecture to capture both local
and long-distance contexts for CNER. Second, we propose a unified framework to
jointly train CNER and word segmentation models in order to enhance the ability
of CNER model in identifying entity boundaries. Third, we introduce an
automatic method to generate pseudo labeled samples from existing labeled data
which can enrich the training data. Experiments on two benchmark datasets show
that our approach can effectively improve the performance of Chinese named
entity recognition, especially when training data is insufficient.

本文提出了一种神经方法来解决中文命名实体识别的挑战，该方法包括使用 CNN-LSTM-CRF 神经架构来捕捉 CNER 的本地和长距离上下文，引入统一框架来联合训练 CNER 和分词模型以增强 CNER 模型在识别实体边界方面的能力，以及使用自动方法从现有标记数据生成伪标记样本来扩充训练数据。实验结果表明，该方法特别适用于训练数据不足的情况下，可以有效提高中文命名实体识别的性能。