Natural Language Processing (AI)
Natural Language Processing - answeris a way for computers to analyze, understand,
and derive meaning from human language in a smart and useful way.
NLP Goal - answerto create computers/systems that can understand human language
and communicate with humans in a natural way.
NLP Use Cases - answer- spam detection
- language translation
- virtual agents and chatbots
NLP Use Cases - answer- Text Classification
- Language Modeling
- Speech Recognition
- Document Summarization and Question Answering
- Machine Translation
- Caption Generation
Topic Modeling - answera technique used to discover prominent and underlying topics
within a collection of documents without any prior knowledge or labeled examples.
Model - answermathematical representation of the learning that has been acquired
Fundamentals of Linguistics - answer- Syntax
- Semantics
- Pragmatics
Syntax - answerstudies sentence structure and the rules governing how words combine
to form meaningful expressions
Semantics - answerfocuses on the study of meaning in language. It explores how
words, phrases, and sentences convey information and represent concepts
Pragmatics - answerdeals with how language is used in context and how the speaker,
the listener, and the surrounding situation influence meaning
Supervised Learning - answer- trained on labeled dataset
- output is predicted by the supervised learning model
- predict outcomes for new data
- Regression and classification tasks
Unsupervised Learning - answer- trained on unlabeled texts
, - hidden patterns are discovered using the unsupervised model
- Finding useful insights, hidden patterns from the unknown dataset.
- Clustering and association tasks
Supervised Learning - answeris so named because the data scientist acts as a guide to
teach the algorithm what conclusions it should come up with.
Text Classification - answeraka Text Categorization, is the activity of labeling natural
language texts with relevant categories from a predefined set.
POS Tagging - answeris a process of assigning a part of speech or lexical class marker
to each word in a sentence (and all sentences in a corpus).
Parsing - answerAnalyzing the grammatical structure of a sentence and identifying its
constituent parts.
Parts-of-speech Tagging - answerMeaning of the POS Tagging acronym.
Named Entity Recognition (NER) - answeris to process a text and identify named
entities in a sentence. Named entities are specific pieces of information, such as names
of people, organizations, locations, dates, and more.
Word sense disambiguation - answerDetermining the correct meaning of a word based
on its context
Types of Classification - answer- Binary
- Multiclass and Multi-label
Binary Classification - answerclassifying data into two mutually exclusive groups or
categories
Multiclass and Multi-label Classification - answerreviewing textual data and assigning
one (single label) or more (multi) labels to the textual data.
Text Summarization - answerAutomatically generating a summary of a text that
captures the most important information.
Natural Language Generation - answerUsing computer algorithms to automatically
generate natural language text, such as news articles or product descriptions
Text Classification Application - answer1. Language Detection
2. Sentiment Analysis
3. Spam Filtering
4. Email Routing
Dataset - answerA ________ to provide examples for training the classifier.
Natural Language Processing - answeris a way for computers to analyze, understand,
and derive meaning from human language in a smart and useful way.
NLP Goal - answerto create computers/systems that can understand human language
and communicate with humans in a natural way.
NLP Use Cases - answer- spam detection
- language translation
- virtual agents and chatbots
NLP Use Cases - answer- Text Classification
- Language Modeling
- Speech Recognition
- Document Summarization and Question Answering
- Machine Translation
- Caption Generation
Topic Modeling - answera technique used to discover prominent and underlying topics
within a collection of documents without any prior knowledge or labeled examples.
Model - answermathematical representation of the learning that has been acquired
Fundamentals of Linguistics - answer- Syntax
- Semantics
- Pragmatics
Syntax - answerstudies sentence structure and the rules governing how words combine
to form meaningful expressions
Semantics - answerfocuses on the study of meaning in language. It explores how
words, phrases, and sentences convey information and represent concepts
Pragmatics - answerdeals with how language is used in context and how the speaker,
the listener, and the surrounding situation influence meaning
Supervised Learning - answer- trained on labeled dataset
- output is predicted by the supervised learning model
- predict outcomes for new data
- Regression and classification tasks
Unsupervised Learning - answer- trained on unlabeled texts
, - hidden patterns are discovered using the unsupervised model
- Finding useful insights, hidden patterns from the unknown dataset.
- Clustering and association tasks
Supervised Learning - answeris so named because the data scientist acts as a guide to
teach the algorithm what conclusions it should come up with.
Text Classification - answeraka Text Categorization, is the activity of labeling natural
language texts with relevant categories from a predefined set.
POS Tagging - answeris a process of assigning a part of speech or lexical class marker
to each word in a sentence (and all sentences in a corpus).
Parsing - answerAnalyzing the grammatical structure of a sentence and identifying its
constituent parts.
Parts-of-speech Tagging - answerMeaning of the POS Tagging acronym.
Named Entity Recognition (NER) - answeris to process a text and identify named
entities in a sentence. Named entities are specific pieces of information, such as names
of people, organizations, locations, dates, and more.
Word sense disambiguation - answerDetermining the correct meaning of a word based
on its context
Types of Classification - answer- Binary
- Multiclass and Multi-label
Binary Classification - answerclassifying data into two mutually exclusive groups or
categories
Multiclass and Multi-label Classification - answerreviewing textual data and assigning
one (single label) or more (multi) labels to the textual data.
Text Summarization - answerAutomatically generating a summary of a text that
captures the most important information.
Natural Language Generation - answerUsing computer algorithms to automatically
generate natural language text, such as news articles or product descriptions
Text Classification Application - answer1. Language Detection
2. Sentiment Analysis
3. Spam Filtering
4. Email Routing
Dataset - answerA ________ to provide examples for training the classifier.
