Conversation with Merlin [email protected] · Wed Dec 06 2023

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It appears that the context provided is an excerpt from Charles Dickens' novel "A Tale of Two Cities." The quote "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness..." is from the famous opening lines of the book.

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From Wikipedia, the free encyclopedia Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP).[1] Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction. Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains. An example is the extraction from newswire 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 automated reasoning about the logical form 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 is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR)[2] has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to understand an attack article only enough to find data corresponding to the slots in this template. History[edit] Information extraction dates back to the late 1970s in the early days of NLP.[3] An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc with the aim of providing real-time financial news to financial traders.[4] Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference[5] that focused on the following domains: MUC-1 (1987), MUC-3 (1989): Naval operations messages. MUC-3 (1991), MUC-4 (1992): Terrorism in Latin American countries. MUC-5 (1993): Joint ventures and microelectronics domain. MUC-6 (1995): News articles on management changes. MUC-7 (1998): Satellite launch reports. Considerable support came from the U.S. Defense Advanced Research Projects Agency (DARPA), who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism.[citation needed] Present significance[edit] The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the World Wide Web, refers to the existing Internet as the web of documents [6] and advocates that more of the content be made available as a web of d

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Have an information extraction problem in mind? Want to leverage NLP along with OCR & NER to automate information extraction? Working with an enormous amount of text data is always hectic and time-consuming. Hence, many companies and organisations rely on Information Extraction techniques to automate manual work with intelligent algorithms. Information extraction can reduce human effort, reduce expenses, and make the process less error-prone and more efficient.This article will delve into building information extraction algorithms on unstructured data using OCR, Deep Learning and NLP techniques. It will also cover use-cases, challenges and discuss how to set up information extraction NLP workflows for your business.Table of Contents What is Information Extraction? How Does Information Extraction Work? Setting up an Information Extraction Workflow A few applications of Information Extraction Conclusion Have an OCR problem in mind for information extraction? Want to digitize invoices, PDFs or number plates? Head over to Nanonets and build OCR models for free!Information Extraction is the process of parsing through unstructured data and extracting essential information into more editable and structured data formats.For example, consider we're going through a companys financial information from a few documents. Usually, we search for some required information when the data is digital or manually check the same. But with information extraction NLP algorithms, we can automate the data extraction of all required information such as tables, company growth metrics, and other financial details from various kinds of documents (PDFs, Docs, Images etc.).Below is a screenshot explaining how we can extract information from an Invoice.Information Extraction WorkflowInformation Extraction from text data can be achieved by leveraging Deep Learning and NLP techniques like Named Entity Recognition. However, if we build one from scratch, we should decide the algorithm considering the type of data we're working on, such as invoices, medical reports, etc. This is to make sure the model is specific to a particular use case. Well be learning more about this in the following sections.To understand the mechanics of Information Extraction NLP algorithms, we should understand the kind of data we are working on. This will help us to sort out the information we want to extract from the unstructured data. For example, for invoice related information, the algorithm should understand the invoice items, company name, billing address etc. While working on medical reports, it should identify and extract patient names, drug information, and other general reports. After curating the data, well then start applying the information extraction NLP techniques, to process and build models around the data. Below are some of the most common techniques that are frequently used.TokenizationComputers usually won't understand the language we speak or communicate with. Hence, we break the language, basically the words and sentences, into tokens and then load it into a program. The process of breaking down language into tokens is called tokenization. For example, consider a simple sentence: "NLP information extraction is fun''. This could be tokenized into:One-word (sometimes called unigram token): NLP, information, extraction, is, funTwo-word phrase (bigram tokens): NLP information, information extraction, extraction is, is fun, fun NLP Three-word sentence (trigram tokens): NLP information extraction, information extraction is, extraction is funimport spacy nlp = spacy.load("en_core_web_sm") doc = nlp("Apple is looking at buying U.K. startup for $1 billion") for token in doc: print(token.text)Apple is looking at buying U.K. startup for $ 1 billion Parts of Speech TaggingTagging parts of speech is very crucial for information extraction from text. It'll help us understand the context of the text data. We usually refer to text from documents as ''unstructured data'' data with

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Knowledge center Data integration What is Data Integration? What is Data Extraction? Definition and We have access today to more data than ever before. The question is: how do we make the most of it? For many, the biggest challenge lies in finding a data integration tool that can manage and analyze many types of data from an ever-evolving array of sources. But before that data can be analyzed or used, it must first be extracted. In this article, we define the meaning of the term data extraction and examine the ETL process in detail to understand the critical role that extraction plays in the data integration process.What is Data Extraction?Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured. Data extraction makes it possible to consolidate, process, and refine data so that it can be stored in a centralized location in order to be transformed. These locations may be on-site, cloud-based, or a hybrid of the two.Data extraction is the first step in both ETL (extract, transform, load) and ELT (extract, load, transform) processes. ETL/ELT are themselves part of a complete data integration strategy.Data Extraction and ETLTo put the importance of data extraction in context, its helpful to briefly consider the ETL process as a whole. In essence, ETL allows companies and organizations to 1) consolidate data from different sources into a centralized location and 2) assimilate different types of data into a common format. There are three steps in the ETL process:Extraction: Data is taken from one or more sources or systems. The extraction locates and identifies relevant data, then prepares it for processing or transformation. Extraction allows many different kinds of data to be combined and ultimately mined for business intelligence.Transformation: Once the data has been successfully extracted, it is ready to be refined. During the transformation phase, data is sorted, organized, and cleansed. For example, duplicate entries will be deleted, missing values removed or enriched, and audits will be performed to produce data that is reliable, consistent, and usable.Loading: The transformed, high quality data is then delivered to a single, unified target location for storage and analysis.The ETL process is used by companies and organizations in virtually every industry for many purposes. For example, GE Healthcare needed to pull many types of data from a range of local and cloud-native sources in order to streamline processes and support compliance efforts. Data extraction was made it possible to consolidate and integrate data related to patient care, healthcare providers, and insurance claims.Similarly, retailers such as Office Depot may able to collect customer information through mobile apps, websites, and in-store transactions. But without a way to migrate and merge all of that data, its potential may be limited. Here again, data extraction is the key.Data Extraction without ETLCan data extraction take place outside of ETL? The short answer is yes. However, its important to keep in mind the limitations of data extraction outside of a more complete data integration process. Raw data which is extracted but not transformed or loaded properly will likely be difficult to organize or analyze, and may be incompatible with newer programs and applications. As a result, the data may be useful for archival purposes, but little else. If youre planning to move data from a legacy databases into a newer or cloud-native system, youll be better off extracting your data with a complete data integration tool.Another consequence of extracting data as a stand alone process will be sacrificing efficiency, especially if youre planning to execute the extraction manually. Hand-coding can be a painstaking process that is prone to errors and difficult to replicate across multiple extractions. In other words, the code itself may have to be rebuil

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