Difference between revisions of "User:Frankier"
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One thing that interests me about rule based approaches in comparison to statistical/ML approaches to machine translation and NLP is that a rule based system can explain itself to a language learner (some statistical/ML approaches can learn rules - such hybrid systems might also be able to explain themselves). |
One thing that interests me about rule based approaches in comparison to statistical/ML approaches to machine translation and NLP is that a rule based system can explain itself to a language learner (some statistical/ML approaches can learn rules - such hybrid systems might also be able to explain themselves). |
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== Some pages == |
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* [[Code style]] - trying to document existing Apertium code style and make steps towards a consistent style for Apertium code |
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* [[Jenkins]] - CI for Apertium code and language data |
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* [[CG hybrid tagging]] |
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* [[Perceptron tagger]] |
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* [[Frankier/GSOC 2016 submission]] |
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Latest revision as of 18:21, 22 August 2016
Basic info[edit]
Full name: Frankie Robertson
Email: $first@$second.name
Web: http://frankie.robertson.name
Phone: +358 46576679
IRC: frankier
About me/blurb (cribbed from GSOC application)[edit]
My interest in Natural Language Processing was initially sparked by my attempts to learn the Finnish language which got me pondering language and language learning in general quite a bit. My long term vision that drives my interest is that I think eventually we can apply some of these tools to improve the experience of language learning. The idea is something along the lines of “if we can make it a model for a computer - we can get the computer to teach it to a human”. I don’t think I’m alone in having this long term vision - for example the VISL project http://beta.visl.sdu.dk/ and the Oahpa! Project http://oahpa.no/ have worked in this direction (but I think there is a lot more to be done and different potential ways to apply NLP techniques to help language learners).
Since I have experience in software engineering, programming and computer science, NLP feels like a very direct and natural way to engage with linguistics and linguistic issues.
One thing that interests me about rule based approaches in comparison to statistical/ML approaches to machine translation and NLP is that a rule based system can explain itself to a language learner (some statistical/ML approaches can learn rules - such hybrid systems might also be able to explain themselves).
Some pages[edit]
- Code style - trying to document existing Apertium code style and make steps towards a consistent style for Apertium code
- Jenkins - CI for Apertium code and language data
- CG hybrid tagging
- Perceptron tagger
- Frankier/GSOC 2016 submission
