Chinese entity recognition methods and commonly used data sets
Named entity recognition (NER) is an important task in natural language processing. It aims to identify entities with specific meanings from text, such as names of people, places, organizations, etc. Chinese NER faces more challenges because the Chinese language has special characteristics and requires the use of more language processing technologies and rules to deal with it.
Chinese named entity recognition methods mainly include rule-based, statistics-based and hybrid methods. Rule-based methods identify entities by manually constructing rules or rule templates. Statistics-based methods use machine learning algorithms to learn entity recognition models from large corpora. Hybrid methods combine two methods to take advantage of both rules and statistical learning.
For the specific implementation of Chinese named entity recognition, the following steps can generally be used:
1. Word segmentation: Split the Chinese text into one by one words for subsequent processing.
2. Part-of-speech tagging: Tag each segmented word with a part-of-speech tag for subsequent processing.
3. Entity recognition: Identify entities in the text based on preset rules or trained models.
In the process of entity recognition, you need to pay attention to the following points:
1. Definition of entity categories: It is necessary to determine which entities are needed Identified and classified into different categories, such as names of people, places, organizations, etc.
2. Determination of entity boundaries: It is necessary to determine the starting position and end position of the entity for subsequent labeling of the entity.
3. Solution to the problem of entity duplication: The same entity may appear multiple times in the text, and it needs to be uniformly marked as the same entity to avoid repeated counting.
Chinese named entity recognition is widely used. For example, in natural language processing tasks such as information extraction, information retrieval, text classification, and machine translation, named entity recognition needs to be performed first. At the same time, it is also widely used in social media, news media, advertising and other fields. For example, identifying users' personal information in social media can provide support for precise advertising and marketing; in news reports, identifying the names of people, places, organizations and other entities involved in the event can help users more quickly Understand the background and relevant information of the incident.
Chinese Named Entity Recognition Dataset
The Chinese Named Entity Recognition Dataset is the basis for training and evaluating named entity recognition models. Currently, there are Multiple Chinese named entity recognition datasets are widely used. The following is an introduction to some commonly used Chinese named entity recognition data sets:
1) MSRA-NER data set: MSRA-NER is Chinese named entity recognition data created by Microsoft Research Asia The set contains more than 80,000 news texts, of which more than 60,000 are used for training and more than 20,000 are used for testing. The entity categories of this data set include person names, place names, organization names and other entities.
2) PKU and MSRA’s People’s Daily dataset: This dataset was jointly created by Peking University and Microsoft Research Asia and includes news reports, editorials and comments from People’s Daily and other types of articles. This dataset is large in size and contains more than 500,000 entity annotations.
3) WeiboNER dataset: This dataset was created by Tsinghua University and contains a large amount of Chinese text from Sina Weibo, including names of people, places, organizations, and times. , dates, professional terms and other entity types. The dataset also contains challenging entities such as internet slang and new vocabulary.
4) OntoNotes dataset: This dataset was created by the National Institute of Standards and Technology and contains text data and entity annotations in multiple languages (including Chinese). The dataset is large in size and contains more than 100,000 entity annotations.
5) CCKS 2017 Task 2 Dataset: This dataset was created by the Chinese Information Society of China and is a task of the 2017 CCKS (Chinese Knowledge Graph Research Area of the Chinese Information Society of China) One, including news, encyclopedias, Weibo and other text types, including names of people, place names, organization names and other entity types. This data set is large in size and contains approximately 100,000 entity annotations.
In short, Chinese named entity recognition is an important task in natural language processing. It has a wide range of applications and has important practical significance.
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