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.

Information Extraction with LLM

CLEAR showed over a 70% reduction in both data usage (tokens) and processing time (inference time) compared to the other modern methods tested. this also can help your content over confusing signals. Avoid duplicate content issues; use clear language, avoid jargon, and consider your audience’s level of understanding.

CLEAR RAG Method for Clinical Information Extraction:

Background & Problem:

AI Improvement: Modern AI systems called Large Language Models (LLMs), when combined with a technique called Retrieval-Augmented Generation (RAG), are better at pulling information from text than older methods.
Logic: Think of LLMs like ChatGPT. RAG helps them find the *right documents* first before answering your question about them.
Current Drawback: However, these current RAG methods often use something called “embeddings” to find information, which isn’t always efficient.
Logic: Embeddings are a technical way AI understands data. Sometimes this method makes the AI look through too much irrelevant information, wasting time and computing power.

The New Solution: CLEAR

New Method: A new process called CLinical Entity Augmented Retrieval (CLEAR) has been developed.
How it Works: Instead of relying just on embeddings, CLEAR finds information by focusing on specific “entities.”
Logic: In a medical context, “entities” are specific terms like diseases (“diabetes”), medications (“metformin”), or procedures. CLEAR uses these keywords to search more directly.

In essence: RAG in 2025 won’t just be “find similar stuff and summarize.” It will be a more precise, multi-faceted search process feeding a highly capable LLM that understands how to accurately extract and structure information strictly based on the evidence provided, making it a cornerstone for reliable information extraction from large datasets.

RAG Information Extraction in 2025: A Projection

Retrieval-Augmented Generation (RAG) for information extraction is already significantly more sophisticated and efficient than earlier versions. Here’s the projected workflow:

1. Smarter, Hybrid Information Retrieval Q1 of 2025

Instead of relying solely on basic semantic similarity (embeddings), the ‘retrieval’ step will use a hybrid approach. It will combine:
- Semantic Search: Finding passages with similar meaning to the query.
- Keyword/Lexical Search: Matching exact terms or variations.
- Entity Recognition: Identifying specific entities (like names, dates, organizations, medical terms) relevant to the query within the documents before full retrieval.
- Knowledge Graphs/Structured Data: Directly querying linked data or databases for known facts when applicable.
- Contextual Understanding: The retriever will better understand the intent behind the query to fetch more relevant and diverse snippets.

2. Intelligent Chunking & Re-ranking

Documents will be broken down (chunked) more intelligently, perhaps based on structure or topic shifts.
Initial search results will likely undergo a re-ranking step, potentially using a smaller, faster AI model or the main LLM itself, to prioritize the most relevant snippets specifically for the extraction task.

3. Context-Aware Generation

The Large Language Model (LLM) or ‘generator’ part will receive the original query and the refined, highly relevant retrieved snippets. It will be much better at qestion answering content opportunities. Here are aditional advantages:
- Synthesizing Information: Combining facts accurately from multiple snippets, even if they present slightly different wording or perspectives.
- Strict Grounding: Relying almost exclusively on the provided snippets to generate the extracted information, minimizing hallucination.
- Structured Output: Reliably formatting the extracted information into desired structures (like JSON, lists, tables) based on the prompt.
- Attribution: More clearly citing which retrieved snippet supports each piece of extracted information.

4. Efficiency & Orchestration

The overall process will be faster and use fewer computational resources due to optimized retrieval algorithms, smaller specialized models for certain tasks (like re-ranking), and more efficient LLMs.
RAG systems will likely be part of larger “agentic” workflows, where the RAG process might be called iteratively or as one step in a more complex reasoning chain. It can be used to support knowledge graph building using AI insights.

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. While Google Vertex AI uses various structure data types, it can still leverage schema markup.

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.