ABSTRACT
Pat Suppes used to say 20 years ago that he would pay attention to AI and natural language processing (NLP) only when it could “do something of book length”. That day is now pretty close, though old-fashioned machine translation systems like SYSTRAN [Toma, 1977] met his criterion at a low level many years ago. The present could seem like a rerun (in NLP) of the struggles within machine vision in the late 1970’s: the high-level, top-down paradigm of scene analysis (Guzman, Brady, Waltz, etc.) was crumbling in the face of low-level, bottom-up, arguments posed by Marr. NLP has been told for some time that it should be more scientific “like machine vision,” so perhaps the current emergence of connectionist, statistical and associationist techniques in NLP is a form of progress by virtue of that fact alone.
