AI DevCamp Notes: Natural Language Processing (Week 4)

In week 4 of AI DevCamp, we delved deeply into the topic of Natural Language Processing (NLP).

Let’s take a closer look at the notes from our fourth week and explore the key components and applications of NLP that we covered together.

What is NLP?

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP combines aspects of linguistics, computer science, and machine learning.

For example, when you talk to your smartphone and say, “What’s the weather like today?”, the phone uses NLP to understand your words, interpret their meaning, and provide you with a weather update.

Transitioning into the various components that make up NLP, let’s delve into some foundational elements.

Key Components of NLP

Morphological Analysis:
This examines the structure of words to identify roots and affixes. For example, the word “running” can be broken down into the root “run” and the suffix “ing”.

Syntactic Analysis (Parsing):
Analyzing the grammatical structure of sentences helps understand relationships between words. For example, in the sentence “The cat climbed the tree,” “the cat” is the subject, “the tree” is the object, and “climbed” is the verb.

Semantic Analysis:
This focuses on the meaning of words and sentences by understanding the context to derive accurate meaning. For example, “bank” in “I sat by the bank” (riverbank) vs. “I went to the bank” (financial institution).

Lexical Semantic Ambiguity:
Refers to situations where a word has multiple meanings depending on context. For example, “bat” can mean a flying mammal or a piece of sports equipment. The meaning is determined by the surrounding words: “The bat flew at night” vs. “He hit the ball with a bat.”