User:Frankier - Revision history

Frankier at 18:21, 22 August 2016

2016-08-22T18:21:32Z

Frankier at 11:37, 22 August 2016

2016-08-22T11:37:11Z

Frankier at 11:00, 15 June 2016

2016-06-15T11:00:12Z

Frankier at 10:59, 15 June 2016

2016-06-15T10:59:36Z

Frankier at 13:00, 27 March 2016

2016-03-27T13:00:10Z

Frankier: Created page with "== Basic info == Full name: Frankie Robertson Email: $first@$second.name Web: http://frankie.robertson.name Phone: +358 46576679 IRC: frankier == About me/blurb (cribbed fro..."

2016-03-27T12:59:47Z

Created page with "== Basic info == Full name: Frankie Robertson Email: $first@$second.name Web: http://frankie.robertson.name Phone: +358 46576679 IRC: frankier == About me/blurb (cribbed fro..."

New page

== Basic info ==

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) ==

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).

@@ Line 24: / Line 24: @@
 * [[CG hybrid tagging]]
 * [[Perceptron tagger]]
+* [[Frankier/GSOC 2016 submission]]

@@ Line 23: / Line 23: @@
 * [[Jenkins]] - CI for Apertium code and language data
 * [[CG hybrid tagging]]
+* [[Perceptron tagger]]

@@ Line 20: / Line 20: @@
 == Some pages ==
- * [[Code style]] - trying to document existing Apertium code style and make steps towards a consistent style for Apertium code
+* [[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
+* [[Jenkins]] - CI for Apertium code and language data
- * [[CG hybrid tagging]]
+* [[CG hybrid tagging]]

@@ Line 18: / Line 18: @@
 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 ==
+ * [[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]]

@@ Line 2: / Line 2: @@
 Full name: Frankie Robertson
 Email: $first@$second.name
 Web: http://frankie.robertson.name
 Phone: +358 46576679
 IRC: frankier