Introduction

Abstract

During the ACL 2005 conference, the lifetime achievement award of the Association for Computational Linguistics was assigned to Martin Kay. In his talk, he remarked on the distinction between Computational Linguistics and Natural Language Processing (NLP). Computational Linguistics is about using computers to investigate linguistic theory, while the NLP field concerns the engineering of text processing applications to solve particular tasks for practical reasons. Computational Linguistics is then a science, while NLP is the set of all its technological implications. Computational Linguistics is a branch of general linguistics, while NLP is more properly an engineering problem.

During recent decades, there was some confusion, mostly because of the increasing popularity of empirical methods for text processing. In fact, the expectation of a large portion of the community was that the supervised approach would be successfully applied to any linguistic problem, as long as enough training material was made available. This belief was motivated by the excellent performance achieved by supervised approaches to many traditional NLP tasks, such as Part of Speech Tagging, Machine Translation, Text Categorization, Parsing and many others. Research on empirical methods for NLP has been encouraged by the increasing need for text processing technologies in the Web era. This has induced the community to find some cheap and fast solutions to practical problems, such as mail categorization, question answering and speech recognition. As a result, limited effort has been invested in understanding the basic underlying linguistic phenomena, and the problem of studying the language by exploiting computational approaches (i.e. Computational Linguistics) has been confused with that of implementing useful text processing technologies.