The Natural Language Processing Group at the University of Edinburgh (EdinburghNLP) is a group of faculty, postdocs, and PhD students working on algorithms that make it possible for computers to understand and produce human language. We do research in all core areas of natural language processing, including morphology, parsing, semantics, discourse, language generation, and machine translation. EdinburghNLP also has a strong track record of work at the interface of NLP with other areas, including speech technology, machine learning, computer vision, cognitive modeling, social media, information retrieval, robotics, bioinformatics, and educational technology.

With 11 core faculty members, EdinburghNLP is one of the largest NLP group in the world. It is also ranked as the most productive group in the area, according to csrankings.org. Our achievements include the award-winning neural machine translation system Nematus and the high-performance language modeling toolkit KenLM. EdinbughNLP faculty have a strong record of getting high-profile grants, and have so far won a total of five European Research Council (ERC) grants.

 

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Congratulations, Sameer! Congratulations also to @EdinburghNLP's Li Dong and Mirella Lapata @mlapata for the honourable mention they received at #ACL2018 for their paper Coarse-to-Fine Decoding for Neural Semantic Parsing https://t.co/TNmRMnSnya

Sameer Singh@sameer_

We were awarded honorable mention for best paper at #ACL2018! Also, lots of @uwnlp awardees!

Steedman: “LSTMs work in practice, but can they work in theory?” (File under: things I wish i had said)

Mark Steedman receives the #ACL2018 Lifetime Achievement Award! His 2007 Presidential Address was a classic—looking forward to a great acceptance speech! #NLProc

Great news! I just heard that Mark Steedman got the ACL lifetime achievement award for 2018!! Well deserved! Mark is a truly rare combination of computer scientist, psychologist and linguist with an incredible long view on research in the intersections of these fields.

Algorithms like LSTM and RNN may work in practice. But do they work in theory?, asks M. Steedman #ACL2018

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