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		<title>RSS Feed - Home</title>
		<link>http://www.languagecomputer.com</link>
		<description>Current News entries</description>
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			<title><![CDATA[LCC and 80Legs Announce Extractiv.com @ Web 3.0]]></title>
			<link>http://www.languagecomputer.com/news/8/15/LCC-and-80Legs-Announce-Extractiv-com-Web-3-0.html</link>
			<guid>http://www.languagecomputer.com/news/8/15/LCC-and-80Legs-Announce-Extractiv-com-Web-3-0.html</guid>
			<pubDate>Wed, 27 Jan 2010 17:00:00 +0000</pubDate>
			<category><![CDATA[Press Releases]]></category>
			<description><![CDATA[Extractiv is a new kind of content provisioning service, powered by LCC's content extraction and 80Legs web crawling platform, which is making the Web truly actionable. 
			Language Computer Corporation and 80Legs announced Extractiv.com at the Web 3.0 Conference in Santa Clara on January 27th, 2010. Extractiv is a new kind of content provisioning service, powered by LCC's content extraction and 80Legs web crawling platform, which is making the Web truly actionable.  Rather than simply passively “monitoring” the Web, it delivers the information that really matters to you and your business. With Extractiv, it’s easy to build semantically-aware applications – regardless if you’re a newcomer to the Semantic Web or a deep believer in the power of semantic metadata. Let us show you what we can do for you!

Currently in alpha stage, this service will launch mid-2010.]]></description>
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			<title><![CDATA[LCC Labs Demo: "Set Xpander"]]></title>
			<link>http://www.languagecomputer.com/news/7/15/LCC-Labs-Demo-Set-Xpander.html</link>
			<guid>http://www.languagecomputer.com/news/7/15/LCC-Labs-Demo-Set-Xpander.html</guid>
			<pubDate>Wed, 26 Nov 2008 15:54:32 +0000</pubDate>
			<category><![CDATA[Press Releases]]></category>
			<description><![CDATA[Language Computer today launches a new LCC Labs demo, Set Xpander. Xpander demonstrates the kind of rich semantic knowledge which can be automatically extracted from semi-structured resources like Wikipedia for lexicon and other generation purposes.
			Language Computer today launches a new LCC Labs demo, Set Xpander. Xpander demonstrates the kind of rich semantic knowledge which can be automatically extracted from semi-structured resources like Wikipedia for lexicon and other generation purposes.  Wikipedia as a resource is a goldmine of semantic knowledge which is vital to high performance Information Extraction (IE) systems -- like entity, relation and event extraction.



In this tool, the system first disambiguates examples provided by the user, mapping them to associated Wikipedia entries. Those entries are then "xpanded" to related entries ranked by semantic similarity.



For example, given the input "Avalanche", "Civic" and "Ram", the system must first identify the concepts referred to by the user.  In the case of "Avalanche", Xpander selects "Chevrolet Avalanche" from the almost 50 different entries for this term.  Next, it processes structured and unstructured content in Wikipedia as well as other resources to find highly related articles, such as "Ford Ranger", "Chevrolet Silverado", and numerous other car models.



Leveraging resources in this manner is allowing LCC extraction products to achieve increasingly higher precision and recall, for an increasingly broad set of semantic types.  This type of automatic learning reduces training times for LCC's customizable content extractors to mere minutes, with a process that is easy enough for anyone to learn.]]></description>
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			<title><![CDATA[Language Computer launches OpenCicero API]]></title>
			<link>http://www.languagecomputer.com/news/6/15/Language-Computer-launches-OpenCicero-API.html</link>
			<guid>http://www.languagecomputer.com/news/6/15/Language-Computer-launches-OpenCicero-API.html</guid>
			<pubDate>Fri, 03 Oct 2008 20:31:15 +0000</pubDate>
			<category><![CDATA[Press Releases]]></category>
			<description><![CDATA[Language Computer today announced the launch of it's new OpenCicero API, an open source framework for creating custom Natural Language Processing(NLP) technologies. The framework is meant to provide developers with an easy and intuitive way for integrating existing LCC technologies with their own.
			OpenCicero API website Launched!

Language Computer today announced the launch of it's new OpenCicero API, an open source framework for creating custom Natural Language Processing (NLP) technologies. The framework is meant to provide developers with an easy and intuitive way for integrating existing LCC technologies with their own. "We're excited to be releasing functionality that allows users to create their own NLP based products," said lead developer Kirk Roberts. "There's a limitless amount of natural language data out there and a limitless number of ideas. We want OpenCicero to be the bridge which connects product developers with the deep semantic knowledge set to drive the applications of the future." 

Included with this is the release of an open standard which may be used to bring NLP community together by providing a common interface through which new ideas may be developed and disseminated. The new website (http://www.languagecomputer.com/projects/OpenCicero), built on the latest Trac technology, is meant to facilitate the creation and expansion of this community by providing a place where mashup ideas and service concepts may be shared. Said site developer Matt Titsworth: "The site is meant to be the sandbox where people can show off the toys they've developed using the API, and build the castles which will represent new combinations of technology that have never been seen before." 

About Language Computer Corporation

Language Computer Corporation (LCC) is a privately-owned U.S. company committed to the development of next-generation natural language processing technologies. Over the past 10 years, LCC has remained at the forefront of work in NLP, having created state-of-the-art systems for Named Entity Recognition, Coreference Resolution, Information Extraction, Question Answering, and Multi-document Summarization for English, Arabic, Chinese, Farsi, and Korean-language documents.

For More Information

For more information about Language Computer, its products, or its customers, please contact us at (972) 231-0052 or via email at pr@languagecomputer.com.]]></description>
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			<title><![CDATA[Language Computer Unveils New Website]]></title>
			<link>http://www.languagecomputer.com/news/2/15/Language-Computer-Unveils-New-Website.html</link>
			<guid>http://www.languagecomputer.com/news/2/15/Language-Computer-Unveils-New-Website.html</guid>
			<pubDate>Mon, 15 Sep 2008 21:32:13 +0000</pubDate>
			<category><![CDATA[Press Releases]]></category>
			<description><![CDATA[Language Computer Corporation unveiled a redesigned website today, its first in more than 6 years. "This new website should convey to our customers and sponsors the level of excitement that we feel towards our new products.", said Andrew Hickl, Language Computer's CEO and President.
			First Website Re-Launch Since 2002

Language Computer Corporation unveiled a redesigned website today, its first in more than 6 years. "This new website should convey to our customers and sponsors the level of excitement that we feel towards our new products. We feel Language Computer is poised to take the next step forward into a number of markets in the months ahead.", said Andrew Hickl, LCC's CEO and President.

About Language Computer Corporation

Language Computer Corporation (LCC) is a privately-owned U.S. company committed to the development of next-generation natural language processing technologies. Over the past 10 years, LCC has remained at the forefront of work in NLP, having created state-of-the-art systems for Named Entity Recognition, Coreference Resolution, Information Extraction, Question Answering, and Multi-document Summarization for English, Arabic, Chinese, Farsi, and Korean-language documents.

For More Information

For more information about Language Computer, its products, or its customers, please contact us at (972) 231-0052 or via email at pr@languagecomputer.com.]]></description>
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