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Ehud Reiter's Blog

Ehud's thoughts about Natural Language Generation. Also see my book on NLG.

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Author: ehudreiter

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How I Review Papers

Mar 28, 2018 ehudreiterLeave a comment

A few comments on how I review papers (what I actually do, not what I am supposed to do), and associated advice for authors.

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Lexical Choice Needs Machine Learning!

Mar 19, 2018 ehudreiter7 Comments

Lexical choice is an area of NLG which really needs machine-learning and data-based techniques.

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Academic Researchers Should be Scouts and Explorers

Mar 7, 2018 ehudreiter4 Comments

Perhaps being somewhat idealistic, I think academic researchers should act as “scouts” exploring unknown research terrain

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Notes from a Dev Conf: Sensible Attitude to Trendy AI Tech, Arria Presentations

Feb 26, 2018 ehudreiterLeave a comment

I went to my first developers conference last week and was impressed, not least by the sensible attitude towards deep learning and other trendy AI technology.

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Use Good Engineering Methodology When Building NLG Systems!

Feb 8, 2018 ehudreiterLeave a comment

Good software engineering is criticial when building NLG systems, including requirements analysis, design, testing, and support.

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Please Use Two-Tailed P Values!

Jan 29, 2018 ehudreiter1 Comment

If you are writing a scientific paper which presents statistics, please use two-tailed p values unless you **really** know what you are doing.

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How do I Learn about NLG?

Jan 16, 2018Feb 10, 2020 ehudreiter1 Comment

Some suggestions for people who are new to NLG and want to learn more about it.

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Dealing with Edge Cases in NLG

Jan 4, 2018Jan 4, 2018 ehudreiter6 Comments

I give some high-level advice on handling edge cases in NLG. As with any software system, most of the effort in building NLG systems usually goes into handling edge cases.

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Goodbye to a Synagogue

Dec 24, 2017 ehudreiter1 Comment

I was very sad to hear that the Jewish synagogue in the Scottish town of Dundee is likely to close

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Should I Use Deep Learning?

Dec 11, 2017Dec 11, 2017 ehudreiter1 Comment

There is a lot of hype around deep learning. especially at business-oriented AI events. I suggest some questions to think about for companies who are considering using DL.

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