�ݺ�ߣshows by User: estevamhruschka

�ݺ�ߣshows by User: estevamhruschka / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: estevamhruschka / Sun, 13 Sep 2015 20:22:19 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: estevamhruschka Machine Reading the Web: beyond Named Entity Recognition and Relation Extraction /slideshow/machine-reading-the-web-beyond-named-entity-recognition-and-relation-extraction/52730253 mrw-150913202219-lva1-app6892
The Web is inundated with information in many different formats including semi-structured and unstructured data. Machine Reading is a research area that aims to build systems that can read natural-language-based information, extracting knowledge and storing it into knowledge bases. Thus, Machine Reading systems are developed to produce language- understanding technology that will automatically process text in affordable time. In this tutorial the idea of automatically reading the Web using Machine Reading techniques will be explored. Four of the most successful Machine Reading approaches in- tended to Read the Web (namely KnowItAll, Yago, NELL and DBPedia systems) will be presented and discussed. The principles, the subtleties, as well as current results of each approach will be addressed. On-line resources (from each approach) will be explored and the future directions in each system will be pointed out. YAGO, KnowItAll, NELL and DBPedia are not the only research efforts focusing on Reading the Web. They were selected, to be presented in this tutorial, because they show four different and very relevant approaches to this problem, but it does not mean they are the only relevant approaches at all. In spite of mainly focusing on the four aforementioned systems, some other independent contributions on the Read the Web idea will be mentioned and pointed out as related works.]]>
The Web is inundated with information in many different formats including semi-structured and unstructured data. Machine Reading is a research area that aims to build systems that can read natural-language-based information, extracting knowledge and storing it into knowledge bases. Thus, Machine Reading systems are developed to produce language- understanding technology that will automatically process text in affordable time. In this tutorial the idea of automatically reading the Web using Machine Reading techniques will be explored. Four of the most successful Machine Reading approaches in- tended to Read the Web (namely KnowItAll, Yago, NELL and DBPedia systems) will be presented and discussed. The principles, the subtleties, as well as current results of each approach will be addressed. On-line resources (from each approach) will be explored and the future directions in each system will be pointed out. YAGO, KnowItAll, NELL and DBPedia are not the only research efforts focusing on Reading the Web. They were selected, to be presented in this tutorial, because they show four different and very relevant approaches to this problem, but it does not mean they are the only relevant approaches at all. In spite of mainly focusing on the four aforementioned systems, some other independent contributions on the Read the Web idea will be mentioned and pointed out as related works.]]> Sun, 13 Sep 2015 20:22:19 GMT /slideshow/machine-reading-the-web-beyond-named-entity-recognition-and-relation-extraction/52730253 estevamhruschka@slideshare.net(estevamhruschka) Machine Reading the Web: beyond Named Entity Recognition and Relation Extraction estevamhruschka The Web is inundated with information in many different formats including semi-structured and unstructured data. Machine Reading is a research area that aims to build systems that can read natural-language-based information, extracting knowledge and storing it into knowledge bases. Thus, Machine Reading systems are developed to produce language- understanding technology that will automatically process text in affordable time. In this tutorial the idea of automatically reading the Web using Machine Reading techniques will be explored. Four of the most successful Machine Reading approaches in- tended to Read the Web (namely KnowItAll, Yago, NELL and DBPedia systems) will be presented and discussed. The principles, the subtleties, as well as current results of each approach will be addressed. On-line resources (from each approach) will be explored and the future directions in each system will be pointed out. YAGO, KnowItAll, NELL and DBPedia are not the only research efforts focusing on Reading the Web. They were selected, to be presented in this tutorial, because they show four different and very relevant approaches to this problem, but it does not mean they are the only relevant approaches at all. In spite of mainly focusing on the four aforementioned systems, some other independent contributions on the Read the Web idea will be mentioned and pointed out as related works. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mrw-150913202219-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Web is inundated with information in many different formats including semi-structured and unstructured data. Machine Reading is a research area that aims to build systems that can read natural-language-based information, extracting knowledge and storing it into knowledge bases. Thus, Machine Reading systems are developed to produce language- understanding technology that will automatically process text in affordable time. In this tutorial the idea of automatically reading the Web using Machine Reading techniques will be explored. Four of the most successful Machine Reading approaches in- tended to Read the Web (namely KnowItAll, Yago, NELL and DBPedia systems) will be presented and discussed. The principles, the subtleties, as well as current results of each approach will be addressed. On-line resources (from each approach) will be explored and the future directions in each system will be pointed out. YAGO, KnowItAll, NELL and DBPedia are not the only research efforts focusing on Reading the Web. They were selected, to be presented in this tutorial, because they show four different and very relevant approaches to this problem, but it does not mean they are the only relevant approaches at all. In spite of mainly focusing on the four aforementioned systems, some other independent contributions on the Read the Web idea will be mentioned and pointed out as related works.

Machine Reading the Web: beyond Named Entity Recognition and Relation Extraction from Estevam Hruschka

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0 NELL: The Never-Ending Language Learning System /slideshow/nell-46136378/46136378 nell-150322085815-conversion-gate01
This was presented as the Keynote Speech of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana 2014 (the video of this talk is available at VideoLectures: http://t.co/WjDtnjie2k), organized by Marko Grobelnik and Dunja Mladenic. The website for the Conference is: http://ailab.ijs.si/dunja/SiKDD2014/]]>
This was presented as the Keynote Speech of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana 2014 (the video of this talk is available at VideoLectures: http://t.co/WjDtnjie2k), organized by Marko Grobelnik and Dunja Mladenic. The website for the Conference is: http://ailab.ijs.si/dunja/SiKDD2014/]]> Sun, 22 Mar 2015 08:58:15 GMT /slideshow/nell-46136378/46136378 estevamhruschka@slideshare.net(estevamhruschka) NELL: The Never-Ending Language Learning System estevamhruschka This was presented as the Keynote Speech of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana 2014 (the video of this talk is available at VideoLectures: http://t.co/WjDtnjie2k), organized by Marko Grobelnik and Dunja Mladenic. The website for the Conference is: http://ailab.ijs.si/dunja/SiKDD2014/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nell-150322085815-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This was presented as the Keynote Speech of the Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD), Ljubljana 2014 (the video of this talk is available at VideoLectures: http://t.co/WjDtnjie2k), organized by Marko Grobelnik and Dunja Mladenic. The website for the Conference is: http://ailab.ijs.si/dunja/SiKDD2014/

NELL: The Never-Ending Language Learning System from Estevam Hruschka

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0 Automatically Labeling Facts in a Never-Ending Langue Learning system /slideshow/automatically-labeling-facts-in-a-neverending-langue-learning-system/46136218 conversing-150322084844-conversion-gate01
This was presented as an invited seminar at Google Mountain View, hosted by Kevin Murphy, in February 2014.]]>
This was presented as an invited seminar at Google Mountain View, hosted by Kevin Murphy, in February 2014.]]> Sun, 22 Mar 2015 08:48:44 GMT /slideshow/automatically-labeling-facts-in-a-neverending-langue-learning-system/46136218 estevamhruschka@slideshare.net(estevamhruschka) Automatically Labeling Facts in a Never-Ending Langue Learning system estevamhruschka This was presented as an invited seminar at Google Mountain View, hosted by Kevin Murphy, in February 2014. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/conversing-150322084844-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This was presented as an invited seminar at Google Mountain View, hosted by Kevin Murphy, in February 2014.

Automatically Labeling Facts in a Never-Ending Langue Learning system from Estevam Hruschka

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0 Machine Reading the Web /slideshow/machine-reading-the-web/45282538 mrw-150301093430-conversion-gate02
Tutorial presented at WWW2013 (http://www2013.org/program/machine-reading-the-web/)]]>
Tutorial presented at WWW2013 (http://www2013.org/program/machine-reading-the-web/)]]> Sun, 01 Mar 2015 09:34:30 GMT /slideshow/machine-reading-the-web/45282538 estevamhruschka@slideshare.net(estevamhruschka) Machine Reading the Web estevamhruschka Tutorial presented at WWW2013 (http://www2013.org/program/machine-reading-the-web/) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mrw-150301093430-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Tutorial presented at WWW2013 (http://www2013.org/program/machine-reading-the-web/)

Machine Reading the Web from Estevam Hruschka

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0 Machine Learning, Machine Reading and the Web /slideshow/machine-learning-machine-reading-and-the-web/45282423 mlmrw-150301093009-conversion-gate01
Tutorial presented at IBERAMIA'2012 (http://iberamia2012.dsic.upv.es/)]]>
Tutorial presented at IBERAMIA'2012 (http://iberamia2012.dsic.upv.es/)]]> Sun, 01 Mar 2015 09:30:09 GMT /slideshow/machine-learning-machine-reading-and-the-web/45282423 estevamhruschka@slideshare.net(estevamhruschka) Machine Learning, Machine Reading and the Web estevamhruschka Tutorial presented at IBERAMIA'2012 (http://iberamia2012.dsic.upv.es/) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mlmrw-150301093009-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Tutorial presented at IBERAMIA'2012 (http://iberamia2012.dsic.upv.es/)

Machine Learning, Machine Reading and the Web from Estevam Hruschka

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0 https://cdn.slidesharecdn.com/profile-photo-estevamhruschka-48x48.jpg?cb=1499432609 https://cdn.slidesharecdn.com/ss_thumbnails/mrw-150913202219-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-reading-the-web-beyond-named-entity-recognition-and-relation-extraction/52730253 Machine Reading the We... https://cdn.slidesharecdn.com/ss_thumbnails/nell-150322085815-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/nell-46136378/46136378 NELL: The Never-Ending... https://cdn.slidesharecdn.com/ss_thumbnails/conversing-150322084844-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/automatically-labeling-facts-in-a-neverending-langue-learning-system/46136218 Automatically Labeling...