Difference between Information Retrieval and Information Extraction
Extraction means “pulling out” and Retrieval means “getting back.” Information retrieval is about returning the information that is relevant for a specific query or field of interest of the user. While information extraction is more about extracting general knowledge (or relations) from a set of documents or information. Information extraction is the standard process of taking data and extracting structured information from it so that it can be used for various purposes, one of which may be in a search engine.
Information Retrieval :
Information Retrieval refers to the human-computer interaction (HCI) that happens when we use a machine to search some piece of information for information objects (content) that match our search query. It is all about retrieving information that is stored in a database or computer and related to the user’s needs. A user’s query is matched against a set of documents to find the relevant documents. Note that this can result can be a form of a set of documents.
The initial set of documents/texts and the query which says “what to retrieval for” this both things are very important parts of the information retrieval system. It is searching and finding relevant documents from a set of documents. There are various methods and techniques used in information retrieval. In an information retrieval system, we reduce information overload using an automated IR system.
- Precision –
It is number of document retrieved and relevant to user’s information need divided by total number of document that is retrieved.
- Recall –
It is number of document retrieved and relevant to user’s information need divided by total number of relevant document in whole document set.
Various techniques used in information retrieval are:
- Vector space retrieval
- Boolean space retrieval
- Term-document matrix
- Block-sort based indexing
- Tf-idf indexing
- Various clustering methods
Information Extraction :
Information Extraction’s main goal is to find out meaningful information from the document set. IE is one type of IR. IE automatically gets structured information from a set of unstructured documents or corpus. IE focuses more on texts that can be read and written by humans and utilize them with NLP (natural language processing). But information retrieval system finds information that is relevant to the user’s information need and that is stored into a computer. It returns documents of text (unstructured form) from a large set of corpses.
The information extraction system used in online text extraction should come at a low cost. It needs to have flexibility in development and must have an easy conversion to new domains. Let’s take the natural language processing of the machine as an example, i.e. Here IE(information extraction) is able to recognize the IR system of a person’s need. Using information extraction we want to make a machine capable of extracting structured information from documents. The importance of an information extraction system is determined by the growing amount of information available in unstructured form(data without metadata), like on the Internet. This knowledge can be made more accessible utilizing transformation into relational form, or by marking-up with XML tags.
We always try to use automated learning systems in information extraction and we always use this. This type of IE system will decrease the faults in information extraction. This will also reduce dependencies on a domain by diminishing the requirement for supervision. IE of structured information relies on the basic content management principle: “Content must be in context to have value“. Information Extraction is difficult than Information Retrieval.
Difference between Information Retrieval and Information Extraction :
Information Extraction is not Information Retrieval. Conventional text extraction methods also return a set of a subset of documents that are probably relevant to the query. Result return is based on search keywords.
The main goal of IE is to extract meaningful information from corps of documents that might be in different languages. Here meaningful information contains types of information like events, facts, components, or relations. These facts are then usually stored automatically into a database, which may then be used to analyze the data for trends, to give a natural language summary, or simply to serve for online access. More formally, Information Extraction gets facts out of documents while Information Retrieval gets sets of relevant documents.
|Information Retrieval||Information Extraction|
|1.||Document Retrieval||Feature Retrieval|
|2.||Return set of relevant documents||Return facts out of documents|
|3.||The goal is to find documents that are relevant to the user’s information need||The goal is to extract pre-specified features from documents or display information.|
|4.||Real information is buried inside documents||Extract information from within the documents|
|5.||The long listing of documents||Aggregate over the entire set|
|6.||Used in many search engines – Google is the best IR system for the web.||Used in database systems to enter extracted features automatically.|
|7.||Typically uses a bag of words model of the source text.||Typically based on some form of semantic analysis of the source text.|
|8.||Mostly use the theory of information, probability, and statistics.||Emerged from research into rule-based systems.|
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