"Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources."
Identifying important information from a piece of text and organizing it in a structured format.
Text Preprocessing: This involves cleaning the input text by removing stop words, stemming, and lemmatization.
Named Entity Recognition: NER is a technique used to identify and classify named entities such as people, organizations, and locations in text.
Part of Speech Tagging: POS tagging involves identifying the parts of speech of each word in a sentence.
Dependency Parsing: This involves creating a tree-like structure that represents the syntactic relationships between words in a sentence.
Entity Linking: This involves identifying references in text to entities in a knowledge base or other external sources.
Co-reference Resolution: This involves identifying pronouns and other words that refer to the same entities in a text.
Relation Extraction: This involves identifying semantic relationships between entities in a text.
Sentiment Analysis: This involves analyzing the emotional tone of text and determining whether it is positive, negative or neutral.
Topic Modeling: This involves identifying the main topics or themes in a text.
Text Classification: This involves categorizing text into predefined categories or classes.
Knowledge Representation: This involves representing knowledge in a machine-readable format, such as an ontology or semantic network.
Lexical Semantics: This involves analyzing the meanings of words and the relationships between them.
Machine Learning for Natural Language Processing: This involves using machine learning algorithms to build models that can predict or classify text.
Information Retrieval: This involves retrieving relevant information from a large collection of text based on a user's query.
Text Summarization: This involves creating a shortened version of a longer text while preserving the most important information.
Deep Learning for Natural Language Processing: This involves using deep neural networks to process and analyze text data.
Named Entity Recognition (NER): Identifying and categorizing entities mentioned in text, such as people, organizations, dates, and locations.
Relationship Extraction: Identifying and extracting relationships between entities in text, such as marital relationships, ownership, and employment.
Event Extraction: Extracting information about events mentioned in text, including the actors involved, the time and location of the event, and the outcome.
Sentiment Analysis: Identifying and categorizing the sentiment expressed in text, such as positive, negative, or neutral.
Text Classification: Categorizing text into predefined categories, such as news articles, reviews, or opinion pieces.
Information Extraction from Web Pages: Retrieving and extracting specific information from web pages, such as product prices or contact information.
Question Answering: Answering a user's question by extracting information from a given text source.
Summarization: Creating a short summary of a larger text by extracting the most relevant information.
Opinion Mining: Identifying and extracting opinions expressed in text, particularly in relation to products, services, and brands.
Entity Disambiguation: Identifying and disambiguating entities with similar names or descriptions, particularly in the context of natural language processing.