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MetaGooFil is a metadata extraction tool that is written by the same folks who brought us the Harvester.
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The problem of extracting meeting information from unstructured emails, mentioned in Chapter 9, Probabilistic methods, is just one example: many other information extraction tasks have similar conditional random field formulations.ĭr.Patrick Engebretson, in The Basics of Hacking and Penetration Testing (Second Edition), 2013 MetaGooFilĪnother excellent information gathering tools is “MetaGooFil”. Since their introduction, conditional random fields have been and remain one of the dominant tools in this area. There is no real consensus about what text mining covers: broadly interpreted, all natural language processing comes under the ambit of text mining. In certain tightly constrained situations, such as Internet job postings for computing-related jobs, information extraction based on a few manually constructed training examples can compete with an entire manually constructed database in terms of the quality of the rules inferred. These rules might predict the values for certain slot-fillers from the rest of the text.
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Taking information extraction a step further, the extracted information can be used in a subsequent step to learn rules-not rules about how to extract information but rules that characterize the content of the text itself. These constraints may involve the words themselves, their part-of-speech tags, and their semantic classes. These rules may be couched in pattern-action form, the patterns expressing constraints on the slot-filler and words in its local context. Machine learning has been applied to information extraction by seeking rules that extract fillers for slots in the template. These are usually simple enough to be captured by shallow parsing techniques such as small finite-state grammars, although matters may be complicated by ambiguous pronoun references and attached prepositional phrases and other modifiers. Typical extraction problems require finding the predicate structure of a small set of predetermined propositions. Once the entities have been found, the text is parsed to determine relationships among them. The task of identifying the composite structure, which can often be represented as a template with slots that are filled by individual pieces of structured information, is called information extraction.
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Many short documents describe a particular kind of object or event, combining entities into a higher-level composite that represent the document’s entire content. As just one example, what could be simpler than looking up a name in a table? But the name of the former Libyan leader Muammar Qaddafi is represented in 47 different ways in documents that have been received by the Library of Congress! Even the simplest task opens up opportunities for learning to cope with the huge variation that real-life documents present. Regular expressions suffice for artificial constructs such as uniform resource locators (URLs) explicit grammars can be written to recognize dates and sums of money. Another is to use capitalization and punctuation patterns for names and acronyms titles ( Ms.), suffixes ( Jr.), and baronial prefixes ( von) or unusual language statistics for foreign names. How can textual entities be identified? Rote learning, i.e., dictionary lookup, is one idea, particularly when coupled with existing resources-lists of personal names and organizations, information about locations from gazetteers, or abbreviation and acronym dictionaries. They can aid searching, interlinking and cross-referencing between documents. These terms act as single vocabulary items, and many document processing tasks can be significantly improved if they are identified as such. In addition, there are countless domain-specific entities, such as international standard book numbers (ISBNs), stock symbols, chemical structures, and mathematical equations.
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Ordinary documents are full of such terms: phone numbers, fax numbers, street addresses, email addresses, email signatures, abstracts, tables of contents, lists of references, tables, figures, captions, meeting announcements, Web addresses, and more. The idea of metadata is often expanded to encompass words or phrases that stand for objects or “entities” in the world, leading to the notion of entity extraction. Metadata is a kind of highly structured (and therefore actionable) document summary. Metadata was mentioned above as data about data: in the realm of text the term generally refers to salient features of a work, such as its author, title, subject classification, subject headings, and keywords. Pal, in Data Mining (Fourth Edition), 2017 Information ExtractionĪnother general class of text mining problems is metadata extraction.