Imagine a world where your computer understands not just the words you type or speak but also the meaning behind them. Thanks to Natural Language Processing (NLP), this is no longer science fiction but an exciting reality. From the smart assistants on our phones like Siri and Alexa to advanced AI models like ChatGPT, NLP is revolutionizing how we interact with technology.
NLP is the bridge that allows machines to interpret, understand, and generate human language in a way that is both meaningful and useful. This technology is built on two fundamental pillars: syntax and semantics. Syntax deals with the structural rules that dictate how sentences are formed, while semantics focuses on the meaning conveyed by these sentences.
In this blog, we'll dive into the basics of syntax and semantics in NLP. We'll explore how these concepts work together to power the latest advancements in AI, making it possible for technologies like ChatGPT to understand and respond to human language with remarkable accuracy.
Syntax
Syntax refers to the structure of a sentence rather than the meaning of sentence. It deals with the study of how words combine to form sentences and phrases according to the rules of grammar.
1. Tokenization:
Definition: The process of splitting text into individual units (tokens) such as words or sub-words.
Example:
Sentence: "I love NLP."
Tokens: ["I", "love", "NLP", "."]
Types
White Space Tokenization
This method uses white spaces within a string as delimiters to split the text into individual tokens (words).
Example:
Sentence: "I like natural language processing."
Tokenized: ["I", "like", "natural", "language", "processing", "."]
Punctuation-Based Tokenization
This method splits a sentence into word tokens based on punctuation marks and white spaces.
Example:
Sentence: "Hello, world! It's a beautiful day."
Tokenized: ["Hello", ",", "world", "!", "It", "'", "s", "a", "beautiful", "day", "."]
Treebank Word Tokenization
This method separates punctuation and symbols from the text without interference from the textual context. It handles contractions and other special cases.
Example:
Sentence: "They can't be serious!"
Tokenized: ["They", "ca", "n't", "be", "serious", "!"]
By using these tokenization methods, different aspects of the text are preserved and can be further processed for NLP tasks. Each method has its own use case depending on the complexity and requirements of the analysis.
2. Part-of-Speech (POS) Tagging:
Definition: Assigning grammatical categories (e.g., noun, verb, adjective) to each word in a sentence. It is used to handle syntactic ambiguity.
Example:
Sentence: "She enjoys reading books."
POS Tags: ["She/PRP", "enjoys/VBZ", "reading/VBG", "books/NNS"]
Types of Syntactic Ambiguity
Lexical Ambiguity: When a word has more than one meaning.
Example: "He saw the man with the telescope."
Did he use a telescope to see the man, or did he see a man who had a telescope?
Structural Ambiguity: When a sentence can have multiple grammatical structures.
Example: "Old men and women."
Does it mean [old men] and [women] or [old men and old women]?
Attachment Ambiguity: When it's unclear which part of the sentence a particular phrase or word should be associated with.
Example: "She hit the man with the book."
Did she use the book to hit the man, or did she hit a man who had a book?
Semantics
Semantics refers to the meaning of sentence while syntax is about the providing rules for text. There are 5 factors to consider in semantics. These factors help in understanding and processing the meaning of language.
1. Verifiability
Definition: The ability of a system to verify a statement based on a given model or knowledge base.
Example:
Statement: "Is it possible to study artificial intelligence at IU?"
Verifiability: The system checks a knowledge base to confirm if AI courses are offered at IU and responds accordingly.
2. Ambiguity
Definition: The presence of multiple possible meanings for a word, phrase, or sentence.
Example:
Sentence: "I saw her duck."
Ambiguity: It can mean seeing her lower her head quickly or seeing the duck that belongs to her.
3. Canonical Forms
Definition: Standardized representations of different expressions with the same meaning.
Example:
Original Sentences:
"I want to study Artificial Intelligence at IU."
"My goal is to learn about AI at IU."
Canonical Form: "Study AI at IU."
4. Inference
Definition: The ability of a system to draw conclusions from various inputs based on a knowledge base, even if the conclusions are not explicitly represented.
Example:
Question: "Where can I study artificial intelligence?"
Inference: The system uses a knowledge base to infer and list universities that offer AI programs.
5. Expressiveness
Definition: The ability of a system to handle a wide range of topics and understand various ways that meaning can be conveyed.
Example:
Sentences:
"I want to study Artificial Intelligence at IU."
"My goal is to learn about AI at IU."
Expressiveness: The system understands that both sentences mean the same thing and responds appropriately.
Conclusion
Syntax and semantics are foundational yet distinct aspects of Natural Language Processing (NLP). While syntax focuses on the structural rules that govern sentence formation, semantics is concerned with the meaning conveyed by these structures. Both play crucial roles in enabling machines to understand and generate human language effectively.
Together, syntax and semantics form the backbone of NLP, enabling the development of sophisticated applications like chatbots, virtual assistants, and translation services. By integrating structural analysis with meaning extraction, NLP systems can achieve a deeper and more nuanced understanding of human language, paving the way for more natural and effective human-computer interactions.