Comment The Myth of Labor (Score 1) 31
As a former Computational Linguistics student, I'd say the main problem is either the lack of computational power or the lack of manual labour. Ie.: even a very well defined liguistic area needs to be defined with too many rules (in a complex system) or needs too much data and CPU time (in a brute force) to be feaible, commercially viable, interesting in the Turing-sense... too much effort to just make it work.
This is a very popular opinion, conirmed by the amount of money corporations through at manual processes like taxonomy maintenance and training, but it's out of date. There are several scalable, commercially viable approaches that do not require manual labor or prohivitive processing to be feasible.
Google News is an example of a large-scale application built with automated NLP. Think Tank 23 makes a NLP-based, ad-hoc categorization engine that powers, among other sites, the Waypath Project.
Talk about a killer app!
This is a very popular opinion, conirmed by the amount of money corporations through at manual processes like taxonomy maintenance and training, but it's out of date. There are several scalable, commercially viable approaches that do not require manual labor or prohivitive processing to be feasible.
Google News is an example of a large-scale application built with automated NLP. Think Tank 23 makes a NLP-based, ad-hoc categorization engine that powers, among other sites, the Waypath Project.
Talk about a killer app!
