Natural Language Processing (NLP): An Important Branch of AI

1 Introduction to Algorithms

Natural Language Processing (NLP) is an interdisciplinary field that combines computer science, artificial intelligence, and linguistics. It primarily studies how to enable computers to understand, process, generate, and simulate human language, thereby achieving the ability to engage in natural conversations with humans. The underlying principles of natural language processing involve multiple levels, including linguistics, computer science, and statistics. It encompasses research on the structure, semantics, syntax, and pragmatics of language, as well as statistical analysis and model building based on large-scale corpora. In the implementation process, multiple levels of processing are required for natural language.

The development of natural language processing can be traced back to the 1950s when computer scientists began attempting to achieve understanding and generation of natural language through computer programs. Early research primarily focused on rule-based and knowledge-based methods, such as writing grammar rules and dictionaries for sentence analysis. In the 1980s, with the improvement of computational power and the emergence of large corpora, statistical methods gradually became dominant in the field of natural language processing. During this period, many statistical-based methods for machine translation, word segmentation, and part-of-speech tagging emerged. Entering the 21st century, especially in the past decade, the development of deep learning technology has greatly propelled the advancement of natural language processing. Models based on deep neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Transformers, have significantly improved the efficiency and accuracy of natural language processing.

Internationally, tech giants like Google, Facebook, and OpenAI have also achieved a series of important breakthroughs in the field of natural language processing. For example, Google’s BERT model and OpenAI’s GPT series models have surpassed human-level performance on multiple natural language processing tasks.

Domestically, research and industrial development in natural language processing have also yielded fruitful results. Currently, there are many research institutions and companies in China focusing on natural language processing, such as the Institute of Computing Technology of the Chinese Academy of Sciences, Tsinghua University, Baidu, and Tencent. Among them, Baidu’s ERNIE and Alibaba’s BERT pre-trained models have demonstrated outstanding performance on various Chinese natural language processing tasks. Meanwhile, many domestic companies have already applied natural language processing technology in scenarios such as intelligent customer service, search engines, and recommendation systems.

2 Algorithm Implementation

The logical flow of natural language processing typically includes the following steps:

Data collection and preprocessing: Obtaining and cleaning raw language data, including text, corpora, or speech data;

Tokenization and lexical analysis: Converting raw text data into a format suitable for model input, such as tokenization, removing stop words, stemming, etc.

Feature extraction: Converting text into a vector form that can be processed by computers, such as word vector representation, sentence vector representation, etc. Common feature extraction methods include bag-of-words model, TF-IDF, word embeddings, etc.

Model training: Utilizing training datasets to train natural language processing models using machine learning or deep learning methods.

Model evaluation: Evaluating the model’s performance using validation datasets, such as accuracy, recall, F1 score, and other metrics.

Model application: Applying the trained model to practical problems, such as text classification, sentiment analysis, machine translation, etc.

3 Applications

Through natural language processing technology, various applications such as machine translation, question-answering systems, sentiment analysis, and text summarization can be realized. With the development of deep learning technology, artificial neural networks and other machine learning methods have made significant progress in the field of natural language processing. Future development directions include deeper semantic understanding, better dialogue systems, broader cross-language processing, and more powerful transfer learning techniques.

In the field of traditional Chinese medicine (TCM), the application of natural language processing is primarily reflected in named entity recognition, relationship extraction, and clustering analysis, which can be used for mining ancient TCM texts, constructing TCM knowledge graphs, and conducting text clustering analysis. For example, in the study of “Shang Han Lun,” researchers utilized natural language processing methods for information extraction and knowledge presentation. This not only organized the content of “Shang Han Lun” but also provided reference methods for converting ancient TCM texts into structured clinical data. Researchers annotated “Shang Han Lun” according to the BIO annotation rules and identified entities within the text using various neural network models, analyzing from both entity types and model performance, ultimately concluding that Bert-Bi LSTM-CRF is the best model for entity recognition in “Shang Han Lun.” After completing named entity recognition, relationships between entities were extracted using rule-based methods. The construction of knowledge graphs was achieved through the neo4j database, realizing the goal of knowledge visualization in “Shang Han Lun.” The Naive Bayes algorithm was used to calculate the weights between prescriptions and symptoms, which were then inserted as attributes into the relationships between prescriptions and symptoms, further refining the construction of the knowledge graph.

4 Conclusion

Currently, the application of natural language processing technology in TCM is still in its early stages, but it serves as an effective tool in the study of TCM texts. When utilizing natural language processing technology for research on TCM texts, two aspects can be focused on: first, the construction of word vectors for TCM texts; second, the extraction of entity relationships in TCM texts.

At present, there are many models for constructing word vectors, but most of them use corpora from Wikipedia or Baidu Encyclopedia, which do not fully match the characteristics of TCM texts. Therefore, combining the corpus with models to build TCM-specific word vectors can be explored. In the application of natural language processing, any application relies on the extraction of entity relationships in TCM texts. Therefore, researching a model with high accuracy and high transferability for entity relationships in TCM texts is of great significance for the intelligent development of TCM.

References:

[1] Zhihu Column. “What is Natural Language Processing? This Article is Enough!” Retrieved on October 24, 2023. https://zhuanlan.zhihu.com/p/634689142.

[2] Qu Qianqian. Research on “Shang Han Lun” Based on Natural Language Processing [D]. Anhui University of Traditional Chinese Medicine, 2022. DOI:10.26922/d.cnki.ganzc.2021.000192.

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