Foundations of NLP

- Overview 

Natural language processing (NLP) uses linguistics and mathematics to bridge human language and computer language. Natural language generally comes in two forms: text or speech. Through NLP algorithms, these natural forms of communication are broken down into data that machines can understand.

There are many complexities in working with natural language, especially for humans who are not used to adapting speech to algorithms. While we can create programs with the rules of speech and written text, humans don't always follow these rules. Linguistics is the study of official and unofficial rules of language. 

The problem with using formal linguistics to create NLP models is that the rules of any language are complex. The rules of language themselves often pose problems when converted into formal mathematical rules. While the rules of language do a good job of defining how an ideal person in an ideal world would speak, human language is also full of shortcuts, inconsistencies, and errors.

- Underlying Mathematics of NLP

Natural Language Processing (NLP) is an interdisciplinary field that uses techniques from computer science, artificial intelligence (AI), and linguistics to enable machines to understand, interpret, and generate human language. 

Here, computers are given the ability to understand human text and spoken language. NLP uses rule-based modeling, statistical learning, and deep learning (DL) models. These models are essential for computers to understand spoken language.

Since machine learning (ML) uses data to learn the mathematical relationship between input and output, it is necessary to understand the underlying mathematics. 

At the core of NLP are numerous mathematical techniques that form the backbone of various algorithms and models. From vector space models and probabilistic methods to neural networks and evaluation metrics, each technique plays an important role in enabling machines to process and understand human language. 

As NLP continues to advance, the integration of advanced mathematical concepts will further enhance its capabilities, drive innovation, and improve the efficiency of language-based applications. 

- Natural Language Processing and Computational Linguistics

In Natural Language Processing (NLP), computational linguistics is the theoretical study of language using computational methods, focusing on understanding the underlying linguistic structures and mechanisms to develop better algorithms for processing human language, while NLP itself is more focused on applying these computational techniques to build practical tools and applications like machine translation, sentiment analysis, and information extraction; essentially, computational linguistics provides the theoretical foundation for NLP development.

Key characteristics about Computational Linguistics in NLP:

• Focus on Language Theory: Computational linguistics delves deeper into linguistic concepts like syntax, semantics, morphology, and pragmatics to model them computationally, whereas NLP primarily focuses on applying these models to solve real-world problems.
• Interdisciplinary Field: It draws from disciplines like linguistics, computer science, mathematics, artificial intelligence, cognitive science, and psychology to analyze language through computational lenses.
• Applications in NLP: Research in computational linguistics contributes to advancements in various NLP tasks including machine translation, speech recognition, text summarization, question answering, dialogue systems, and sentiment analysis.

Example of Computational Linguistics research areas:

• Developing formal grammars: Creating computational representations of language rules to analyze sentence structure and meaning.
• Corpus analysis: Studying large collections of text to identify patterns and trends in language usage
• Semantic modeling: Representing the meaning of words and phrases in a computational framework
• Discourse analysis: Examining how meaning is constructed across multiple sentences in a text

[Painting is silent poetry, and poetry is painting that speaks - Claude Monet]

- The Building Blocks of NLP

The building blocks of Natural Language Processing (NLP) include: tokenization (breaking text into individual words or units), stemming/lemmatization (reducing words to their base form), Named Entity Recognition (NER) (identifying entities like people, places, and organizations), part-of-speech tagging (labeling words based on their grammatical role), sentiment analysis (determining the emotional tone of text), and syntactic analysis (understanding the grammatical structure of a sentence), all of which contribute to the analysis and interpretation of human language by computers.

Key characteristics about these building blocks:

• Tokenization: The first step in NLP, where text is divided into meaningful units called tokens, which could be words, punctuation marks, or even sub-words depending on the chosen method.
• Stemming/Lemmatization: Techniques used to normalize words by reducing them to their base form, helping to group related words together.
• Named Entity Recognition (NER): Identifying and classifying specific entities within text, such as person names, locations, organizations, and dates.
• Part-of-Speech (POS) Tagging: Assigning grammatical labels to each word in a sentence, like "noun," "verb," "adjective".
• Sentiment Analysis: Determining the overall sentiment of a piece of text, whether it is positive, negative, or neutral.
• Syntactic Analysis: Analyzing the grammatical structure of a sentence, including the relationships between words and phrases.

- The Process of NLP

Natural language processing (NLP) is a machine learning technique that uses computational methods to analyze and understand human language. 

The processes of NLP include:

• Semantic analysis: Determines the meaning of words, phrases, and sentences
• Lexical analysis: Assigns parts of speech to words based on context
• Sentiment analysis: Analyzes the feelings or attitude of a text
• Pragmatics: Interprets text using information from previous steps
• Syntactic analysis: Puts word meanings together based on grammar rules
• Discourse integration: Uses context to understand how words and sentences relate to each other
• Machine translation: Automatically translates text from one language to another

NLP uses linguistic principles to understand the meaning of text. For example, an NLP program might identify "cook" as a verb and "macaroni" as a noun. NLP can be used in many applications, including chatbots, translation tools, and other language-based interactions.