1. Articles in category: Machine Translation

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    1. Architect of Jena Semantic Web Framework Joins TopQuadrant

      TopQuadrant(TM), a leading semantic technology products company, today announced that Dr. Jeremy Carroll has joined TopQuadrant as Chief Product Architect. Carroll has contributed to many Semantic Web standards from the World Wide Web Consortium (W3C) and was the lead architect in the creation of Jena 2.0, an open source Semantic Web framework developed by Hewlett Packard Research Labs. Jena is ...
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    2. Language Weaver and empolis Integrate Automated Translation Capabilities in Search and Customer Support Applications ...

      Language Weaver, a leading software company developing enterprise software for the automated translation of human languages, and empolis, a leading service technology company based in Gütersloh Germany, have entered into a strategic technology agreement that will support multilingual translation of enterprise applications. empolis will integrate Language Weaver's automated translation software ...
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    3. Top 5 Natural Language Processing Applications

      In the last decades, Natural Language Processing (NLP) has been equally hyped and criticized. All in all, many applications emerged in the real world following intense and continued research and development. Here’s a list of the most prominent success stories. Given that this blog is about named entity recognition (NER), itself an NLP application, we would [...]
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    4. Cooccurrence and constructions

      A method and system for performing automatic text analysis is described. A local ranking for one or more contexts with respect to a word and a global ranking for one or more contexts are generated. The rankings are based on the frequency with which the contexts appear in a corpus. A statistic may be generated using the local and global rankings, such as a log ratio rank statistic equal to the logarithm of the global rank divided by local rank, to measure the similarity of contexts with respect to words with which they combine. A source matrix of word to ...
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    5. Teaching machine translation

      Last Fall (2007), I taught an Applications of NLP course to a 50/50 mix of grads and senior undergrads. It was modeled partially after a course that I took from Kevin Knight while a grad student. It was essentially 1/3 on finite state methods for things like NER and tagging, then 1/3 on machine translation, then 1/3 on question answering and summarization. Overall, the course went over fairly well. I had a significant problem, however, teaching machine translation. Here's the problem. Students k
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      Mentions: Kevin Knight
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