{"product_id":"9783319025964","title":"SpringerBriefs in Computer Science","description":"\u003ch1\u003eSpringerBriefs in Computer Science\u003c\/h1\u003e \u003ch2\u003eCortez, Eli; da Silva, Altigran S.\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eA new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors’ approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a number of results are produced to address the IETS problem in an unsupervised fashion. In particular, the authors develop, implement and evaluate distinct IETS methods, namely \u003ci\u003eONDUX\u003c\/i\u003e, \u003ci\u003eJUDIE\u003c\/i\u003e and \u003ci\u003eiForm\u003c\/i\u003e.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eONDUX\u003c\/i\u003e (On Demand Unsupervised Information Extraction) is an unsupervised probabilistic approach for IETS that relies on content-based features to bootstrap the learning of structure-based features. \u003ci\u003eJUDIE\u003c\/i\u003e (Joint Unsupervised Structure Discovery and Information Extraction) aims at automatically extracting several semi-structured data records in the form of continuous text and having no explicit delimiters between them. In comparison with other IETS methods, including \u003ci\u003eONDUX\u003c\/i\u003e, \u003ci\u003eJUDIE\u003c\/i\u003e faces a task considerably harder that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it.\u003ci\u003e iForm\u003c\/i\u003e applies the authors’ approach to the task of Web form filling. It aims at extracting segments from a data-rich text given as input and associating these segments with fields from a target Web form.\u003c\/p\u003e\u003cp\u003eAll of these methods were evaluated considering different experimental datasets, which are used to perform a large set of experiments in order to validate the presented approach and methods. These experiments indicate that the proposed approach yields high qualityresults when compared to state-of-the-art approaches and that it is able to properly support IETS methods in a number of real applications. The findings will prove valuable to practitioners in helping them to understand the current state-of-the-art in unsupervised information extraction techniques, as well as to graduate and undergraduate students of web data management.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2013-11-11\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783319025964\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-319-02597-1\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 94\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":45548508348556,"sku":"9783319025964","price":49.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783319025964.jpg?v=1772897533","url":"https:\/\/lateknightbooks.com\/products\/9783319025964","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}