Introduction to Artificial Intelligence: Natural Language Processing

The development of AI (Artificial Intelligence) technology originated from the scientists’ exploration in the 1950s of machine simulation of human intelligence. In recent years, thanks to the progress of information technology, computer hardware and algorithms, AI has developed from early symbol processing to machine learning and deep learning, and has been widely used in fields such as medical treatment and transportation, greatly promoting social progress. With the stunning debut of ChatGPT chat robot, AI technology has made breakthrough progress in NLP (Natural Language Processing), showing people the huge potential of AI in understanding and generating human language. Now, let’s start the exploration journey of NLP together.

What is

NLP?

Natural language is a language system that humans have naturally evolved in daily life to express their thoughts and communicate. Binary code is a language that computers can directly process, and artificial languages such as programming languages and communication protocols can also be efficiently processed by computers. NLP aims to bridge the huge gap between natural language and artificial language, exploring the theories and methods for effective communication between humans and computers using natural language. It is one of the important research directions in the fields of computer science and artificial intelligence.

Classification of

NLP Tasks

NLP Tasks	Description	Subtasks
Lexical Analysis	Lexical analysis of natural language, is a fundamental task in NLP	Tokenization, New Word Discovery, Morphological Analysis, Part-of-Speech Tagging, Spell Checking
Sentence Parsing	Sentence-level analysis of natural language, includes syntactic analysis and other sentence-level tasks	Chunking, Supervised Labeling, Constituency Parsing, Dependency Parsing, Language Modeling, Language Identification, Sentence Boundary Detection
Semantic Analysis	Analyze and understand the given text to form a formal or distributed representation that expresses its meaning.	Word Sense Disambiguation, Semantic Role Labeling, Abstract Semantic Representation Analysis, First-order Predicate Logic, Frame Semantics Analysis, Vectorized Representation of Words, Sentences, and Paragraphs
Information Extraction	Extract structured information from unstructured text.	Named Entity Recognition (NER), Entity Disambiguation, Terminology Extraction, Coreference Resolution, Relation Extraction, Event Extraction, Sentiment Analysis, Intent Recognition, Slot Filling
Top-level Task	System-level tasks that are directly oriented towards end users and provide natural language processing product services, which involve multiple layers of NLP techniques.	Machine Translation, Text Summarization, Reading Comprehension, Automatic Text Classification, Question Answering Systems, Dialogue Systems, Intelligent Generation Systems

The language model (LM) aims to model the probability distribution of natural language. A language model on a vocabulary set V can be formally constructed as the probability of a word sequence appearing as a sentence. However, the computational complexity of this approach is too high. To address this, the joint probability can be converted into a product of conditional probabilities, and the language model is built by maximizing the conditional probability of the next word occurring.

The Development History of

NLP

1. Early Exploration (1950s-1970s):
The early research on NLP was mainly based on rules, such as ELIZA. These systems simulated human communication by simulating conversations, but the coverage of the rules was limited and they were difficult to deal with complex language phenomena.

2. Statistical Learning Methods (1980s-1990s):
With the improvement of computing power, statistical learning methods became popular, such as Hidden Markov Models (HMM) and Recurrent Neural Networks (RNN), which demonstrated strong capabilities in processing sequential data and capturing temporal dependencies.

3. The Era of Deep Learning (2010s – 2017):
The emergence of deep learning brought revolutionary changes to NLP. Technologies represented by Encoder – Decoder, Gated Recurrent Unit (GRU), and ELMO (Embeddings from Language Models) enabled the models to have the ability to handle complex linguistic features such as polysemy and synonyms and capture complex dependencies in sentences. However, when facing downstream tasks, they still require transfer training.

4. The Era of Large Models (2017 – Present ):
In 2017, Google proposed the Transformer model, which completely changed the research methods of NLP. Transformer adopts a self-attention mechanism to process sequential data, which not only enables parallel computing, thereby greatly improving the training speed of the model, but also greatly expands the model capacity, requiring a huge amount of text data for training. Eventually, large language models can receive various downstream tasks in the form of natural language and answer them with high quality. Large models such as BERT series, GPT series, and LLAMA series are typical representatives.

Main Application

Scenarios

According to different implementation methods, traditional NLP landing applications can be divided into four types: dialogue robots (voice semantic Q&A), reading comprehension, intelligent search, and machine translation. The emergence and popularization of large models have greatly expanded the application scope of NLP and promoted the development of many innovative fields, such as high-quality text creation, multi-round smooth interaction, multimodal interaction, auxiliary scientific research, professional emotion and psychological analysis, auxiliary programming, personalized learning, etc.

At present, LLMs in vertical fields across various industries have begun to emerge in business applications and are demonstrating a rapid development trend. Leveraging its profound industry and technological expertise, WatchData is aligning with the evolving trends of the era and is actively developing LLMs for cryptography, smart cards, and the Internet of Things, collaborating with key research institutes to study foundational model issues, while exploring novel approaches to establishing digital trust in the era of LLM.