Artificial Intelligence - Unit 3
AI_Unit_3
Natural Language Processing - Introduction
NLP: Subfield of AI that focuses on the interaction between computers and human language. It
involves processing and analyzing large amounts of natural language data.
Goal: The primary goal of NLP is to enable computers to understand, interpret, and generate
human languages in a valuable way.
Typical Steps:
1. Audio to Text: When you speak in one language, speech recognition converts the spoken
audio into text.
2. Text Processing (Translation): The text is processed using machine translation algorithms
to translate it into another language.
3. Text to Audio (Optional): The translated text can then be converted back into speech
using text-to-speech synthesis if the output is meant to be spoken.
Applications:
Machine Translation: Converting text or speech from one language to another (e.g., Google
Translate).
Speech Recognition: Converting spoken language into text (e.g., Siri, Google Assistant).
Text Classification: Automatically categorizing text into predefined labels (e.g., spam detection
in emails).
Sentiment Analysis: Identifying and extracting subjective information from text (e.g., opinions in
social media posts).
Chatbots and Virtual Assistants: Conversational agents that can understand and respond to
human language (e.g., customer service bots).
Recent Advances:
NLP models like GPT (Generative Pre-trained Transformers) and BERT (Bidirectional Encoder
Representations from Transformers) have made significant strides in improving the accuracy of
language tasks such as translation, summarization, and content generation.
,Linguistics
• Linguistics: Study of how language works
• Phonetics: The study of the physical sounds of human speech, focusing on how they are
produced, transmitted, and received.
Example: The sound /p/ in the words "pat" and "spat". Phonetics is concerned with the
physical production of the sound. In "pat", the /p/ is aspirated (a small burst of air), whereas in
"spat", the /p/ is unaspirated (no burst of air).
• Phonology: The study of how sounds function within a particular language or languages,
focusing on the rules that govern sound patterns and their meaningful contrasts.
Example: The difference between the /b/ and /p/ sounds in English. In English phonology,
/b/ and /p/ are distinct phonemes, meaning they can change the meaning of words (e.g., "bat"
vs. "pat"). Phonology deals with how sounds function within a particular language.
• Morphological Analysis: Breaking down words into their components (roots, prefixes,
suffixes).
Example: Breaking down the word "running" into its base form "run" and the suffix "-ing“.
Prefix - In + complete, Postfix – happily – happy + ly
, Lexical categories
Non-Lexical
AI_Unit_3
Natural Language Processing - Introduction
NLP: Subfield of AI that focuses on the interaction between computers and human language. It
involves processing and analyzing large amounts of natural language data.
Goal: The primary goal of NLP is to enable computers to understand, interpret, and generate
human languages in a valuable way.
Typical Steps:
1. Audio to Text: When you speak in one language, speech recognition converts the spoken
audio into text.
2. Text Processing (Translation): The text is processed using machine translation algorithms
to translate it into another language.
3. Text to Audio (Optional): The translated text can then be converted back into speech
using text-to-speech synthesis if the output is meant to be spoken.
Applications:
Machine Translation: Converting text or speech from one language to another (e.g., Google
Translate).
Speech Recognition: Converting spoken language into text (e.g., Siri, Google Assistant).
Text Classification: Automatically categorizing text into predefined labels (e.g., spam detection
in emails).
Sentiment Analysis: Identifying and extracting subjective information from text (e.g., opinions in
social media posts).
Chatbots and Virtual Assistants: Conversational agents that can understand and respond to
human language (e.g., customer service bots).
Recent Advances:
NLP models like GPT (Generative Pre-trained Transformers) and BERT (Bidirectional Encoder
Representations from Transformers) have made significant strides in improving the accuracy of
language tasks such as translation, summarization, and content generation.
,Linguistics
• Linguistics: Study of how language works
• Phonetics: The study of the physical sounds of human speech, focusing on how they are
produced, transmitted, and received.
Example: The sound /p/ in the words "pat" and "spat". Phonetics is concerned with the
physical production of the sound. In "pat", the /p/ is aspirated (a small burst of air), whereas in
"spat", the /p/ is unaspirated (no burst of air).
• Phonology: The study of how sounds function within a particular language or languages,
focusing on the rules that govern sound patterns and their meaningful contrasts.
Example: The difference between the /b/ and /p/ sounds in English. In English phonology,
/b/ and /p/ are distinct phonemes, meaning they can change the meaning of words (e.g., "bat"
vs. "pat"). Phonology deals with how sounds function within a particular language.
• Morphological Analysis: Breaking down words into their components (roots, prefixes,
suffixes).
Example: Breaking down the word "running" into its base form "run" and the suffix "-ing“.
Prefix - In + complete, Postfix – happily – happy + ly
, Lexical categories
Non-Lexical
