Module 02

Basics of Natural Language Processing

Understand the fundamental concepts and techniques of NLP that power conversational interfaces.

Learning Objectives

  • Understand the core concepts of Natural Language Processing (NLP)
  • Learn about tokenization, stemming, lemmatization, and part-of-speech tagging
  • Explore intent recognition and entity extraction techniques
  • Understand how NLP enables conversational interfaces
  • Gain practical experience with basic NLP operations

Introduction to Natural Language Processing

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to read, understand, and derive meaning from human languages in a valuable way. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.

For conversational AI, NLP serves as the foundation that allows systems to interpret user inputs, extract meaningful information, and generate appropriate responses. Without NLP, conversational interfaces would be limited to rigid, pre-programmed interactions that couldn't adapt to the natural way humans communicate.

Interactive

Key NLP Challenges in Conversational AI

Ambiguity

Human language is inherently ambiguous. Words, phrases, and sentences can have multiple meanings depending on context.

Example: "Book that flight" could mean "reserve a flight" or "record that flight in a log."

Variability

There are countless ways to express the same idea in natural language.

Example: "What's the weather like?", "How's the weather today?", "Is it going to rain?", "Weather forecast please" all seek similar information.

Context Dependency

Understanding often requires knowledge of previous exchanges or external context.

Example: "Is it open?" only makes sense if we know what "it" refers to from previous conversation.

Implicit Information

Humans often leave information unstated that must be inferred.

Example: "I need to get to the airport" might implicitly be asking for transportation options.

Core NLP Concepts and Techniques

To overcome these challenges, NLP employs various techniques to process and analyze text. Let's explore the fundamental concepts and techniques that form the building blocks of NLP in conversational AI.

Text Preprocessing

Before applying more advanced NLP techniques, raw text typically undergoes preprocessing to normalize and clean the data. Common preprocessing steps include:

  • Noise Removal: Eliminating irrelevant characters, HTML tags, or other non-textual content
  • Text Normalization: Converting text to a standard format (e.g., lowercase)
  • Handling Special Characters: Deciding how to treat punctuation, numbers, and special symbols

Tokenization

Tokenization is the process of breaking down text into smaller units called tokens. These tokens can be words, characters, or subwords. Tokenization is a crucial first step in most NLP pipelines as it creates the basic units for further processing.

Word Tokenization Example


# Python example using NLTK
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

text = "Hello, how can I help you today?"
tokens = word_tokenize(text)
print(tokens)
# Output: ['Hello', ',', 'how', 'can', 'I', 'help', 'you', 'today', '?']
                            

Different tokenization strategies exist for different languages and use cases. For example, character-level tokenization might be more appropriate for Chinese or Japanese, while subword tokenization methods like Byte-Pair Encoding (BPE) or WordPiece are commonly used in modern language models to handle out-of-vocabulary words.

Stemming and Lemmatization

Stemming and lemmatization are techniques used to reduce words to their base or root form, which helps in treating different word forms as a single item.

Stemming involves removing prefixes and suffixes from words to obtain the stem. It's a heuristic process that often results in non-real words.

Stemming Example


# Python example using NLTK
from nltk.stem import PorterStemmer

stemmer = PorterStemmer()
words = ["running", "runs", "runner", "ran", "easily", "fairly"]
stemmed_words = [stemmer.stem(word) for word in words]
print(stemmed_words)
# Output: ['run', 'run', 'runner', 'ran', 'easili', 'fairli']
                            

Lemmatization is more sophisticated and aims to obtain the proper dictionary form of a word, known as the lemma. Unlike stemming, lemmatization considers the context and part of speech of the word.

Lemmatization Example


# Python example using NLTK
import nltk
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()
words = ["running", "runs", "runner", "ran", "better", "goods"]
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
print(lemmatized_words)
# Output: ['running', 'run', 'runner', 'ran', 'better', 'good']
                            
Infographic comparing Stemming (faster, rule-based, e.g., running -> run) and Lemmatization (dictionary-based, more accurate, e.g., better -> good).

Figure 2: Stemming vs. Lemmatization

Knowledge Check: Module 2

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