Information Extraction

in the context of text analytics, is the process of identifying and extracting structured information from unstructured or semi-structured text.

Information Extraction can be used for a variety of purposes, such as to generate reports or summaries from large amounts of text, create training data for machine learning algorithms, or automate the coding of open-ended survey responses.

While the term Information Extraction is most commonly used in the text analytics industry, it may also be used in other contexts, such as data mining or web scraping. In these cases, the term may refer to the process of extracting information from unstructured or semi-structured data sources, such as HTML documents or database tables.

Information Extraction should not be confused with other similar terms, such as data extraction, which refers to the process of extracting data from structured sources, or text mining, which is the process of extracting information from text data using methods such as topic modeling or text classification.

Information Extraction Tools

Tools used in Information Extraction can vary depending on the type and format of the text being processed, as well as the desired output. For example, manual coding is often used when working with small amounts of text, or when high accuracy is required. However, for large volumes of text, NLP-based methods are typically more efficient.

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