Natural Language Processing

How Natural Language Processing Works

Natural language processing aims to transform unstructured language data into a structured format that machines can use to interpret speech and text data, discover and visualize complex relationships in large data sets, and generate new language data.

Raw human language data can come from various sources, including audio signals, web and social media, documents, and databases. The data contains valuable information such as voice commands, public sentiment on topics, operational data, and maintenance reports. Natural language processing can combine and simplify these large sources of data, transforming them into meaningful insights with visualizations and topic models.

To perform natural language processing on speech data, detect the presence of human speech in an audio segment, perform speech-to-text transcription, and apply text mining and machine learning techniques on the derived text.

Data Preparation for Natural Language Processing

Some techniques used in natural language processing to convert text from an unstructured format to a structured format are:

Tokenization: Typically, this is the first step in text processing for natural language processing. It refers to splitting up the text into sentences or words.

Stemming: This text normalization technique reduces words to their root forms by removing affixes of the words. It uses simple heuristic rules and may result in invalid dictionary words.

Lemmatization: This sophisticated text normalization technique uses vocabulary and morphological analysis to remove affixes of words. For example, “building has floors” reduces to “build have floor.”

Word2vec: The most popular implementation among word embedding techniques is Word2vec. The technique creates a distributed representation of words into numerical vectors, which capture semantics and relationships among words.

N-gram modeling: An n-gram is a collection of n successive items in a text document that may include words, numbers, symbols, and punctuation. N-gram models can be useful in natural language processing applications where sequences of words are relevant, such as in sentiment analysis, text classification, and text generation.

Natural Language Processing with AI

AI models trained on language data can recognize patterns and predict subsequent characters or words in a sentence. To build natural language processing models, you can use classical machine learning algorithms, such as logistic regression or decision trees, or use deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. For example, you can use CNNs to classify text and RNNs to generate a sequence of characters.

Transformer models (a type of deep learning model) revolutionized natural language processing, and they are the basis for large language models (LLMs) such as BERT and ChatGPT™. Transformers are designed to track relationships in sequential data. They rely on a self-attention mechanism to capture global dependencies between input and output.

In the context of natural language processing, this allows LLMs to capture long-term dependencies, complex relationships between words, and nuances present in natural language. LLMs can process all words in parallel, which speeds up training and inference.

Similar to other pretrained deep learning models, you can perform transfer learning with pretrained LLMs to solve a particular problem in natural language processing. For example, you can fine-tune a BERT model for Japanese text.