{"product_id":"unsupervised-information-extraction-by-text-segmentation-pdf-version","title":"Unsupervised Information Extraction by Text Segmentation (PDF - Version)","description":"\u003cp\u003e- Delivery by email. Can be DOWNLOAD IMMEDIATELY after purchasing.\u003cbr\u003e- PDF eBook eTextbook Only! NO Online Access. NO Physical Book, Can be read on any devices (Kindle, NOOK, Android\/IOS devices, Windows, MAC).\u003cbr\u003e- WE NOW ACCEPT CREDIT CARD, PAYPAL payments\u003cbr\u003e- If you need EPUB Version, please contact to us.\u003c\/p\u003e\n\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\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eONDUX\u003c\/i\u003e,\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eJUDIE\u003c\/i\u003e\u003cspan\u003e \u003c\/span\u003eand\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eiForm\u003c\/i\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eONDUX\u003c\/i\u003e\u003cspan\u003e \u003c\/span\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.\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eJUDIE\u003c\/i\u003e\u003cspan\u003e \u003c\/span\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\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eONDUX\u003c\/i\u003e,\u003cspan\u003e \u003c\/span\u003e\u003ci\u003eJUDIE\u003c\/i\u003e\u003cspan\u003e \u003c\/span\u003efaces a task considerably harder that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it.\u003ci\u003e\u003cspan\u003e \u003c\/span\u003eiForm\u003c\/i\u003e\u003cspan\u003e \u003c\/span\u003eapplies 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\n\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 quality results 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","brand":"Eli Cortez","offers":[{"title":"Default Title","offer_id":14303357239365,"sku":"9783319025964","price":28.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0037\/4623\/4437\/products\/517lSk9nMSL._SX328_BO1_204_203_200.jpg?v=1537377275","url":"https:\/\/saporo-books.myshopify.com\/products\/unsupervised-information-extraction-by-text-segmentation-pdf-version","provider":"Saporo Books","version":"1.0","type":"link"}