lfcc/ner_archive_pt
收藏资源简介：
--- task_categories: - token-classification language: - pt size_categories: - 100K<n<1M --- ### Dataset This dataset was created by consolidating information from various Portuguese Archives. We gathered data from these archives and subsequently performed manual annotation of each harvested corpus with Named Entities such as Person, Place, Date, Profession and Organization. The resulting dataset was formed by merging all the individual corpora into a unified corpus which we named "ner-archive-pt" and can be accessed at: http://ner.epl.di.uminho.pt/ ### Citation ```bibtex @Article{make4010003, AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos}, TITLE = {NER in Archival Finding Aids: Extended}, JOURNAL = {Machine Learning and Knowledge Extraction}, VOLUME = {4}, YEAR = {2022}, NUMBER = {1}, PAGES = {42--65}, URL = {https://www.mdpi.com/2504-4990/4/1/3}, ISSN = {2504-4990}, ABSTRACT = {The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.}, DOI = {10.3390/make4010003} } ```
任务类别：词元分类（token-classification） 语言：葡萄牙语（pt） 样本规模：10万 < 样本量 < 100万 ### 数据集说明 本数据集通过整合多家葡萄牙档案馆的馆藏信息构建而成。我们从上述档案馆采集数据后，针对所获取的每一个语料库开展人工标注，标注的命名实体（Named Entity）涵盖人物、地点、日期、职业与组织机构等类别。我们将所有独立语料库合并为统一语料库，以此构建最终数据集，并将其命名为「ner-archive-pt」，数据集访问地址为：http://ner.epl.di.uminho.pt/ ### 引用文献 bibtex @Article{make4010003, AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos}, TITLE = {《档案检索工具中的命名实体识别：扩展版》}, JOURNAL = {《机器学习与知识抽取》(Machine Learning and Knowledge Extraction)}, VOLUME = {4}, YEAR = {2022}, NUMBER = {1}, PAGES = {42--65}, URL = {https://www.mdpi.com/2504-4990/4/1/3}, ISSN = {2504-4990}, ABSTRACT = {多年来，葡萄牙档案馆保存的信息量持续增长。这些档案文件承载着该国极具价值的历史遗产，详实记录了葡萄牙的发展历程。目前，多数葡萄牙档案馆已将其档案检索工具以数字化形式向公众开放，但这些数据未经过任何标注，致使其内容分析工作往往难度颇高。本研究针对档案检索工具开发了命名实体识别（Named Entity Recognition, NER）方案，可实现对其中多种命名实体的识别与分类。这些命名实体蕴含着与其上下文相关的关键信息，凭借较高的识别置信度，可应用于多种场景，例如借助实体链接与记录链接技术开发智能浏览工具。为获得优异的模型性能，我们标注了多组语料库，用于训练适配该领域的机器学习算法，同时采用了卷积神经网络（Convolutional Neural Networks, CNN）、长短期记忆网络（Long Short-Term Memory, LSTM）以及最大熵模型（Maximum Entropy Model）等多种架构。最终，我们通过开发的Web平台NER@DI向公众开放了所有构建的数据集与机器学习模型。}, DOI = {10.3390/make4010003} }
数据集概述
- 任务类别: 词性标注 (token-classification)
- 语言: 葡萄牙语 (pt)
- 数据规模: 100K<n<1M
数据集描述
该数据集是通过整合来自多个葡萄牙档案馆的信息创建的。我们从这些档案馆收集数据，并对每个采集的语料库进行手动标注，标注的命名实体包括人名、地点、日期、职业和组织。最终的数据集是通过将所有单独的语料库合并成一个统一的语料库形成的，我们将其命名为“ner-archive-pt”。
引用信息
bibtex @Article{make4010003, AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos}, TITLE = {NER in Archival Finding Aids: Extended}, JOURNAL = {Machine Learning and Knowledge Extraction}, VOLUME = {4}, YEAR = {2022}, NUMBER = {1}, PAGES = {42--65}, URL = {https://www.mdpi.com/2504-4990/4/1/3}, ISSN = {2504-4990}, ABSTRACT = {The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.}, DOI = {10.3390/make4010003} }
