What is meant by Information extraction?
The term "information extraction" refers to the process of filtering and structuring specific, relevant information from unstructured or semi-structured data. The goal is to extract useful information from large volumes of data that can be used for analysis, decision-making, or further processing. Information extraction is commonly used in areas such as text analysis, data processing, and machine learning.
Typical software functions in the area of "information extraction":
- Text Analysis: Identification and extraction of relevant data from texts, such as keywords, entities, or topics.
- Named Entity Recognition (NER): Identification and classification of named entities (e.g., people, organizations, locations) within texts.
- Information Retrieval: Search functions that allow finding relevant information from large data sets.
- Data Cleaning: Removal of irrelevant or redundant information from datasets to improve data quality.
- Knowledge Databases: Creation and maintenance of databases that structure and make extracted information accessible.
- Text Mining: Analysis of texts to discover patterns and relationships and to extract useful information.