Deep Learning for Natural Language Processing: Named Entity Recognition using KoBERT

In this course, we will explore Named Entity Recognition (NER), one of the fields of Natural Language Processing (NLP) that utilizes deep learning. In particular, we will thoroughly explain the basic concepts and implementation methods of NER using the KoBERT model, which is suitable for Korean processing.

1. What is Natural Language Processing (NLP)?

Natural language processing refers to the technology that allows computers to understand and generate human language. This is the process of analyzing the meaning, grammar, and functions of language so that computers can comprehend it. Major applications of natural language processing include machine translation, sentiment analysis, question-answering systems, and named entity recognition.

1.1 What is Named Entity Recognition (NER)?

Named Entity Recognition (NER) is a technology that identifies and classifies proper nouns such as people, places, organizations, and dates in text. For example, in the sentence “Lee Soon-shin won a great victory at the Battle of Hansando,” “Lee Soon-shin” is recognized as a person, while “Hansando” is recognized as a location. NER plays a key role in various fields such as information extraction, search engines, and document summarization.

2. Introduction to KoBERT

KoBERT is a model that has been retrained for Korean based on Google’s BERT model. BERT (Bidirectional Encoder Representations from Transformers) is one of the most popular models in natural language processing, known for its strong ability to understand context. KoBERT has been trained on a Korean dataset to reflect the characteristics of the Korean language and can better grasp the meanings of words.

2.1 Basic Structure of BERT

BERT is based on the Transformer architecture and understands context bidirectionally. This allows the model to better understand context by simultaneously considering the front and back of the input sentence. BERT is trained through two tasks:

  • Masked Language Model (MLM): Some words are hidden, and the model predicts those hidden words.
  • Next Sentence Prediction (NSP): The model predicts whether two sentences are consecutive.

3. Implementing NER using KoBERT

Now, we will explain the process of implementing named entity recognition using KoBERT step by step. For this practical work, we will be using Python and Hugging Face’s Transformers library.

3.1 Setting Up the Environment

!pip install transformers
!pip install torch
!pip install numpy
!pip install pandas
!pip install sklearn

3.2 Preparing the Data

We need to prepare a dataset for training named entity recognition. We will use the publicly available ‘Korean NER Dataset.’ This dataset includes sentences and entity tags for each word.

For example:

Lee Soon-shin B-PER
won O
the B-LOC
Battle O
of O
Hansando O
with O
a O
great O
victory O

3.3 Loading the KoBERT Model

Next, we load the KoBERT model. It can be easily accessed through Hugging Face’s Transformers library.

from transformers import BertTokenizer, BertForTokenClassification
import torch

# Load KoBERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('monologg/kobert')
model = BertForTokenClassification.from_pretrained('monologg/kobert', num_labels=len(tag2id))

3.4 Data Preprocessing

We need to preprocess the data for input into the model. This includes tokenizing the text and encoding the tags.

def encode_tags(tags, max_len):
    return [tag2id[tag] for tag in tags] + [tag2id['O']] * (max_len - len(tags))

# Example data
sentences = ["Lee Soon-shin won a great victory at the Battle of Hansando"]
tags = [["B-PER", "O", "B-LOC", "O", "O", "O", "O", "O", "O"]]

# Initialization
input_ids = []
attention_masks = []
labels = []

for sentence, tag in zip(sentences, tags):
    encoded = tokenizer.encode_plus(
        sentence,
        add_special_tokens=True,
        max_length=128,
        pad_to_max_length=True,
        return_attention_mask=True,
    )
    input_ids.append(encoded['input_ids'])
    attention_masks.append(encoded['attention_mask'])
    labels.append(encode_tags(tag, 128))

3.5 Model Training

We will train the model using the preprocessed data. You can define the loss function and optimizer using PyTorch and train the model.

from sklearn.model_selection import train_test_split

# Split into training and validation data
train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids, labels, test_size=0.1)

# Model training and evaluation code...

3.6 Model Evaluation

After training, we evaluate the model’s performance using validation data. Metrics such as Accuracy, Precision, and Recall can be used for evaluation.

from sklearn.metrics import classification_report

# Model prediction code...
predictions = model(validation_inputs)
predicted_labels = ...

# Output evaluation metrics
print(classification_report(validation_labels, predicted_labels))

3.7 Using the Model

Using the trained model, we can recognize entities in new sentences. This includes the process of predicting entity tags for each word when inputting text.

def predict_entities(sentence):
    encoded = tokenizer.encode_plus(sentence, return_tensors='pt')
    with torch.no_grad():
        output = model(**encoded)
    logits = output[0]
    predictions = torch.argmax(logits, dim=2)
    return predictions

4. Conclusion

In this course, we learned the basic concepts and implementation methods of named entity recognition using KoBERT. Thanks to the powerful performance of KoBERT, we can efficiently perform NER tasks in the field of natural language processing. These technologies can be widely utilized in various business and research areas, demonstrating excellent performance even with Korean data.

5. References

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  • Hugging Face Transformers Documentation
  • KoBERT GitHub Repository
  • Introduction to Natural Language Processing with Deep Learning

6. Additional Learning Resources

There are various materials related to natural language processing, and many resources available for training models suited for different domains. Here are some recommended materials:

  • Stanford CS224n: Natural Language Processing with Deep Learning
  • fast.ai: Practical Deep Learning for Coders
  • CS50’s Introduction to Artificial Intelligence with Python

7. Future Research Directions

Developing more advanced systems based on KoBERT and named entity recognition technology will be an important research direction. Additionally, training and developing multilingual models that can be directly applied to more languages is also an interesting research topic.

8. Q&A

If you have any questions regarding this course, please let me know in the comments. I will actively respond!