Early efforts to find theoretically elegant formal models for various linguistic phenomena did not result in any noticeable progress, despite nearly three decades of intensive research (late 1950’s through the late 1980’s ). As the various formal (and in most cases mere symbol manipulation) systems seemed to reach a deadlock, disillusionment in the brittle logical approach to language processing grew larger, and a number of researchers and practitioners in natural language processing (NLP) started to abandon theoretical elegance in favor of attaining some quick results using empirical (data-driven) approaches.
All seemed natural and expected. In the absence of theoretically elegant models that can explain a number of NL phenomena, it was quite reasonable to find researchers shifting their efforts to finding practical solutions for urgent problems using empirical methods. By the mid 1990’s, a data-driven statistical revolution that was already brewing over took the field of NLP by a storm, putting aside all efforts that were rooted in over 200 years of work in logic, metaphysics, grammars and formal semantics.
We believe, however, that this trend has overstepped the noble cause of using empirical methods to find reasonably working solutions for practical problems. In fact, the data-driven approach to NLP is now believed by many to be a plausible approach to building systems that can truly understand ordinary spoken language. This is not only a misguided trend, but is a very damaging development that will hinder significant progress in the field. In this regard, we hope this study will help start a sane, and an overdue, semantic (counter) revolution.