Named_entity_recognition

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named_entity_recognition is an Open Source Research & Education tool that implements various models for Chinese named entity recognition. It provides concrete implementations of HMM, CRF, BiLSTM, and BiLSTM+CRF models, using a resume dataset for evaluation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is named_entity_recognition?

named_entity_recognition is an open-source project dedicated to Chinese named entity recognition (NER), offering practical implementations of several prominent models. It includes Hidden Markov Model (HMM), Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), and a hybrid BiLSTM+CRF model. The project utilizes a resume dataset for training and evaluation, providing detailed accuracy, recall, and F1 scores for each model. It serves as a valuable resource for researchers and developers interested in NLP, particularly in the context of Chinese NER, allowing for direct comparison and understanding of different algorithmic approaches.

Best used for

Ideal for researchers and developers who need to implement and compare different named entity recognition models, evaluate their performance on Chinese text, and understand the underlying algorithms. Especially valuable for academic studies and practical applications in natural language processing.

Common actions

implement NER models

evaluate NLP models

research Chinese NER

automated workflowcollaborationdeepfakelow-code/no-codeworkflows"AI Agents"github copilotface swappingopen-source

Capabilities

Key features

HMM model implementation
CRF model implementation
BiLSTM model implementation
BiLSTM+CRF model implementation
Chinese NER dataset
Performance evaluation metrics

Target Audience

professordeveloperresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of named entity recognition models are implemented in this project?

This project implements several models for Chinese named entity recognition, including Hidden Markov Model (HMM), Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), and a combined BiLSTM+CRF model, offering a comprehensive comparison.

What dataset is used for training and evaluating the models?

The project utilizes a resume dataset, specifically collected from the ACL 2018 paper 'Chinese NER using Lattice LSTM'. The data is formatted with each character and its corresponding BIOES tag on separate lines.

How can I run and evaluate the models?

After installing dependencies with `pip3 install -r requirement.txt`, you can train and evaluate models by running `python3 main.py`. To load and evaluate a trained model, use `python3 test.py`.

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