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URL Extractor For Web Pages and Text can identify Web citations with the exact same URL but otherwise possibly different. Most differences are not significant, but sometimes different page numbers from the same URL are cited.
Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions.
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." [1] Written resources may include websites, books, emails, reviews, and ...
Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction.The goal of terminology extraction is to automatically extract relevant terms from a given corpus.
The LiveURLs project [42] proposed a fragment identifier format for referring to a region of text within a page, of the form #FWS+C, where F is the length of the first word (up to five characters), W is the first word itself, S is the length of the selected text and C is a 32-bit CRC of the selected text. [43]
A query string is a part of a uniform resource locator that assigns values to specified parameters.A query string commonly includes fields added to a base URL by a Web browser or other client application, for example as part of an HTML document, choosing the appearance of a page, or jumping to positions in multimedia content.
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) [ 3 ] has developed automatic methods, typically of a statistical flavor, for indexing large document collections and ...
Typical unstructured data sources include web pages, emails, documents, PDFs, social media, scanned text, mainframe reports, spool files, multimedia files, etc. Extracting data from these unstructured sources has grown into a considerable technical challenge, where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction ...