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Today I learned about the LSTM+CRF sequence labeling method, which is a powerful model that combines recurrent neural network (RNN) and conditional random field (CRF) to handle sequence labeling problems such as named entity recognition (NER), part-of-speech tagging, etc.
Fundamental：

• LSTM (Long Short-Term Memory): As a type of RNN, LSTM can learn long-distance dependencies in sequences and capture key information in time series data.
• CRF (Conditional Random Field): CRF is a probabilistic graphical model that can learn dependencies between labels, for example, the “大” in “Tsinghua University” should belong to the same entity as “清” and “华”.
  The basic steps：

1. Data preprocessing: Convert the text sequence into word vector representation and perform padding operation to make all sequences of the same length.
2. LSTM Encoding: Use LSTM network to encode word vectors and extract internal representation of sequences.
3. CRF decoding: Use the CRF model to predict the label of each word based on the output of LSTM and the dependency between the labels.
4. Model Training: Use negative log-likelihood loss function to train the model and optimize model parameters.
   example：
   Taking named entity recognition as an example, the input sequence is "Tsinghua University is located in the capital Beijing". The LSTM+CRF model will predict the label of each word. For example, "Tsinghua University" will be labeled as "B-LOC" (beginning of entity) and "I-LOC" (inside entity), while "Beijing" will be labeled as "B-LOC".
   Code execution process：
5. Importing Libraries: Import the MindSpore library and related modules.
6. Defining CRF layers: Implement the forward training and decoding part of the CRF layer, including Score calculation and Normalizer calculation.
7. Defining the Model: Build an LSTM+CRF model, combining LSTM and CRF layers together.
8. data preparation: Generate training data and perform data preprocessing, including converting text into word vectors, padding, and other operations.
9. Model Training: Use the optimizer to train the model and optimize the model parameters.
10. Model Evaluation: Use test data to evaluate model performance, such as calculating accuracy, recall, and other indicators.
    Application Scenario：
    The LSTM+CRF sequence labeling method can be applied to various sequence labeling problems, such as:

• Named Entity Recognition: Identify entities in text, such as names of people, places, organizations, etc.
• Part-of-speech tagging: Label each word in the text with a part of speech, such as noun, verb, adjective, etc.
• Event Extraction: Extract event information from text, such as time, place, people, event type, etc.
  Medical Applications：
  The LSTM+CRF sequence labeling method is also widely used in the medical field, for example:
• Medical text information extraction: Extract key information such as patient symptoms, drug names, treatment methods, etc. from texts such as electronic medical records and medical literature.
• Gene sequence analysis: Analyze gene sequences and identify functional regions in genes, such as coding regions, non-coding regions, etc.
• Protein structure prediction: Predict the three-dimensional structure of proteins and provide reference for drug design.
  In summary, the LSTM+CRF sequence labeling method is a powerful tool that can be applied to various sequence labeling problems and plays an important role in the medical field.

Detailed documentation and code are:
【Tencent Document】LSTM CRF sequence labeling
https://docs.qq.com/pdf/DUm1JdWlxbE5mSHdQ?