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.
The above is the detailed content of Chinese entity recognition methods and commonly used data sets. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
15 recommended open source free image annotation tools
Mar 28, 2024 pm 01:21 PM
Image annotation is the process of associating labels or descriptive information with images to give deeper meaning and explanation to the image content. This process is critical to machine learning, which helps train vision models to more accurately identify individual elements in images. By adding annotations to images, the computer can understand the semantics and context behind the images, thereby improving the ability to understand and analyze the image content. Image annotation has a wide range of applications, covering many fields, such as computer vision, natural language processing, and graph vision models. It has a wide range of applications, such as assisting vehicles in identifying obstacles on the road, and helping in the detection and diagnosis of diseases through medical image recognition. . This article mainly recommends some better open source and free image annotation tools. 1.Makesens
This article will take you to understand SHAP: model explanation for machine learning
Jun 01, 2024 am 10:58 AM
In the fields of machine learning and data science, model interpretability has always been a focus of researchers and practitioners. With the widespread application of complex models such as deep learning and ensemble methods, understanding the model's decision-making process has become particularly important. Explainable AI|XAI helps build trust and confidence in machine learning models by increasing the transparency of the model. Improving model transparency can be achieved through methods such as the widespread use of multiple complex models, as well as the decision-making processes used to explain the models. These methods include feature importance analysis, model prediction interval estimation, local interpretability algorithms, etc. Feature importance analysis can explain the decision-making process of a model by evaluating the degree of influence of the model on the input features. Model prediction interval estimate
Identify overfitting and underfitting through learning curves
Apr 29, 2024 pm 06:50 PM
This article will introduce how to effectively identify overfitting and underfitting in machine learning models through learning curves. Underfitting and overfitting 1. Overfitting If a model is overtrained on the data so that it learns noise from it, then the model is said to be overfitting. An overfitted model learns every example so perfectly that it will misclassify an unseen/new example. For an overfitted model, we will get a perfect/near-perfect training set score and a terrible validation set/test score. Slightly modified: "Cause of overfitting: Use a complex model to solve a simple problem and extract noise from the data. Because a small data set as a training set may not represent the correct representation of all data." 2. Underfitting Heru
The evolution of artificial intelligence in space exploration and human settlement engineering
Apr 29, 2024 pm 03:25 PM
In the 1950s, artificial intelligence (AI) was born. That's when researchers discovered that machines could perform human-like tasks, such as thinking. Later, in the 1960s, the U.S. Department of Defense funded artificial intelligence and established laboratories for further development. Researchers are finding applications for artificial intelligence in many areas, such as space exploration and survival in extreme environments. Space exploration is the study of the universe, which covers the entire universe beyond the earth. Space is classified as an extreme environment because its conditions are different from those on Earth. To survive in space, many factors must be considered and precautions must be taken. Scientists and researchers believe that exploring space and understanding the current state of everything can help understand how the universe works and prepare for potential environmental crises
Transparent! An in-depth analysis of the principles of major machine learning models!
Apr 12, 2024 pm 05:55 PM
In layman’s terms, a machine learning model is a mathematical function that maps input data to a predicted output. More specifically, a machine learning model is a mathematical function that adjusts model parameters by learning from training data to minimize the error between the predicted output and the true label. There are many models in machine learning, such as logistic regression models, decision tree models, support vector machine models, etc. Each model has its applicable data types and problem types. At the same time, there are many commonalities between different models, or there is a hidden path for model evolution. Taking the connectionist perceptron as an example, by increasing the number of hidden layers of the perceptron, we can transform it into a deep neural network. If a kernel function is added to the perceptron, it can be converted into an SVM. this one
Implementing Machine Learning Algorithms in C++: Common Challenges and Solutions
Jun 03, 2024 pm 01:25 PM
Common challenges faced by machine learning algorithms in C++ include memory management, multi-threading, performance optimization, and maintainability. Solutions include using smart pointers, modern threading libraries, SIMD instructions and third-party libraries, as well as following coding style guidelines and using automation tools. Practical cases show how to use the Eigen library to implement linear regression algorithms, effectively manage memory and use high-performance matrix operations.
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
Is Flash Attention stable? Meta and Harvard found that their model weight deviations fluctuated by orders of magnitude
May 30, 2024 pm 01:24 PM
MetaFAIR teamed up with Harvard to provide a new research framework for optimizing the data bias generated when large-scale machine learning is performed. It is known that the training of large language models often takes months and uses hundreds or even thousands of GPUs. Taking the LLaMA270B model as an example, its training requires a total of 1,720,320 GPU hours. Training large models presents unique systemic challenges due to the scale and complexity of these workloads. Recently, many institutions have reported instability in the training process when training SOTA generative AI models. They usually appear in the form of loss spikes. For example, Google's PaLM model experienced up to 20 loss spikes during the training process. Numerical bias is the root cause of this training inaccuracy,
