Information Extraction – Basic Definition
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.
In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video could be seen as information extraction. Due to the difficulty of the problem, current approaches to IE focus on narrowly restricted domains.
An example is the extraction from news wire reports of corporate mergers, such as denoted by the formal relation: , from an online news sentence such as: “Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp.” A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow logical reasoning to draw inferences based on the logical content of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context.
“Information extraction” refers to the meachine learning process of automatically extracting structured data (i.e. names, dates, entities, locations) from unstructured or semi-structured text sources, like documents or web pages. This aids computers in understanding and analyzing the information in a more organized way; essentially, it’s the practice of taking raw text and converting it into a structured format that can be easily used by machines.
What is an information extraction process?
Various systems and search engines have unique information extraction processes. Overall, it is the process of extracting specific (pre-specified) information from textual sources. A simple example is when your email extracts only the data from the message for you to add in your Calendar. It is also how Google Image Search works.
What traditionally is the best way to extract information?
People have used various open source and paid tools and methods used for data extraction for a long time. Typically, this includes web scraping, document parsing, text extraction, and API-based data extraction. The internet is the fastest way that most people obtain information. Today, gaing information on the web is faster than ever. Named-entity recognition (NER) is a secondary task of information extraction that attempts to locate and classify atomic elements in text into predefined categories. Categories are often the names of persons, organizations, places, expressions of times, quantities, monetary values, percentages, facts, etc. NER is also known as entity identification and entity extraction.
Here is a good definition according to Springer:
“Information extraction (IE) is the process of automatically extracting structured pieces of information from unstructured or semi-structured text documents. Classical problems in information extraction include named-entity recognition (identifying mentions of persons, places, organizations, etc.) and relationship extraction (identifying mentions of relationships between such named entities). Web information extraction is the application of IE techniques to process the vast amounts of unstructured content on the Web. Due to the nature of the content on the Web, in addition to named-entity and relationship extraction, there is growing interest in more complex tasks such as extraction of reviews, opinions, and sentiments.” – Web Information Extraction
How Information Extraction is Changing
The rapid growth of online data is transforming industries and enterprises worldwide. However, its true potential lies in how people extract, analyze, and apply data. In a world of Gemini, OpenAI and ChatGPT, AI data extraction is emerging as a cornerstone technology, redefining the way businesses access and leverage information. By 2025, the integration of AI into data extraction processes will not just give websites a competitive advantage – it will be vital. Already is IE is foundational to Google Entity Search.
It relies on complex processes of automatically identifying and extracting key pieces of information (entities) by using natural language processing techniques to understand the context and relationships within the text, effectively allowing Google to build a structured knowledge graph about the world.
On the Google Cloud system, data extraction accuracy and performance, on non-parent labels, can add a description (such as added context, insights, and prior knowledge for each entity) – for types of entities it should pick up. Google Cloud data set search can also help with data extraction. During the extraction phase low level
NLP functions occur, such as POS (part of speech) tagging, tokenisation, text comparrisons, sentence boundary detection, capitalization rules and in-document coreference. Cosine similarity also is a critical measure when evaluating the semantic similarity between high-dimensional embeddings for better matching information extraction.
JSON-LD markup aids the extraction, and proposes to users a set of candidate kb entities for a mention.
How is Information Extraction Different from Information Retrieval?
While both information extraction (IE) and information retrieval (IR) are concerned with finding relevant information from data, the core difference is that information extraction focuses on extracting specific, structured data from unstructured text, like identifying entities and relationships, while information retrieval focuses on finding documents or data that closely match a user’s search query. Then a search engine, like Google Search, can return a list of relevant results; essentially, IE is about extracting specific details from text, while IR is about finding the most relevant documents based on a search term.
SEO’s are skilled at query entity extraction and keyword analysis. Advancing technical optimization helps Google’s AI systems to effectively crawl, understand, and extract information from your content. This is what helps your web pages get indexed. SEO incorporate natural language processing to aid information extraction.