Principle-Based Parsing: Natural Language Processing for the 1990s (original) (raw)
Related papers
The papers in this collection are all devoted to single theme: logic and its applications in computational linguistics. They share many themes, goals and techniques, and any editorial classification is bound to highlight some connections at the expense of other. Nonetheless, we have found it useful to divide these papers (somewhat arbitrarily) into the following four categories: logical semantics of natural language, grammar and logic, mathematics with linguistic motivations, and computational perspectives. In this introduction, we use this four-way classification as a guide to the papers, and, more generally, to the research agenda that underlies them. We hope that the reader will find it a useful starting point to the collection.
1993
Programme Committee received a large number of submissions (5 page extended abstracts) from all over the world. The general quality of the submissions was high. Out of a total of 229 submissions, 47 were accepted, including 7 reserve papers. Every abstract submitted was reviewed by one member of the Programme Committee and three referees (see pages v and vi). Electronic submission and reviewing procedures helped to speed up this process and turned out not to cause an unreasonable work load at our centre. We trust that the resulting programme offers an inspiring cross-section of excellent work in the field. The programme features invited talks and thematic sessions around two prominent themes in contemporary research: the relations between logic and computational linguistics, and the use of data-oriented methods in CL. The thematic orientation is further developed in the tutorial sessions which are scheduled the days preceding the conference (19-20 April 1993). New elements compared ...
A state of the art in Computational Linguistics
Linguistics today: facing a greater challenge, 2004
Computational Linguistics has a long history, dating back to the Fifties, during which it developed a whole set of computational models and implementations, theories, methodologies and applications. It is difficult to give a sensible account of its present state without going back a little to the main steps through which this discipline evolved towards its present state. Since its origins, Computational Linguistics has been in an intermediate position between Computer Science and Artificial Intelligence, Linguistics and Cognitive Science, and Engineering. Computer Science itself shares its roots with Computational Linguistics; parsing, which is central for the design of compilers for programming languages (Aho and Ullmann 1977: 6), is also the building block of any natural language processing engine, and both are the realizations of the chomskian theory of formal languages (Chomsky 1957). The same theory, together with the corresponding computational model, has given a contribution to the general hypothesis of Artificial Intelligence, that human behaviours usually judged intelligent could be simulated in a computer in a principled way. Oversimplifying, Artificial Intelligence aims at modelling a number of behaviours through three very general paradigms, theorem proving, problem solving and planning, and language understanding and production. The history of both disciplines is rich in intersections, especially between language processing and planning, as in SHRDLU (Winograd 1971) or, more recently, in ARGOT (Allen et al. 1982, Allen 1983), with all its practical and theoretical follow-ups; modern dialogue systems in all their forms and applications are derived from the dialogue model J.Allen designed for ARGOT. The commitment to "simulation of behaviour", shared by Artificial Intelligence and and a relevant part of Computational Linguistics, makes them also share the effort for "cognitive modelling" of different human behaviours, including the use of language. This is probably one of the reasons why Linguistics appears in the set of sciences originally interested in the arising of the new discipline called Cognitive Science (www.cognitivesciencesociety.org). Since the Seventies, when language technology reached a state of maturity such as to allow the realization of some applications, Engineering has been interested in some of the language processing techniques, and it appeared soon that the approach introduced by engineers was certainly less theoretically and cognitively interesting, but more effective in many ways. By now, we can say that while Computational Linguists were, and are, more interested in the correctness and plausibility of their models, Engineers were, and are, more interested in the usability of tools and techniques, even
Parsing English. Course Notes for a Tutorial on Computational Semantics, March 17-22, 1975
1975
The course in parsing English is essentially a survey and comparison 'of several of the principal systems used for understanding natural language. The basic procedure of parsing is described. The discussion of the principal systems is based on the idea that "meaning is procedures," that is, that the procedures of application give a parsed structure its significance.' Natural language systems should be content-rather than structure-motivated, i.e. they should be concerned with linguistic problems revealed by parsing rather than with the relation of the proposed structure of the system to the structures of other systems. Within this framework, Winograd's understanding system, SHRDLU, is described and discussed, as are the second generation systems of Simmons, Schank, Colby and Wilks. A subsequent discussion compares all these systems. Concluding remarks outline immediate problems, including the need for a good memory model and the use of texts, rather than individual example Sentences, for investigation. (CLK) * * via the ERIC Document Reproduction Service (EDRS). EDRS is pot * responsible for the quality of the original document. Reproductions * * supplied by EDRS are the best that can be made from the original.
22nd International Conference on Computational Linguistics
... Santaholma, Marianne Starlander and Nikos Tsourakis) Grammar and Output Representations in the C&C CCG Parser (Laura Rimell and Stephen Clark) Developing a Modular Parsing System for Semantic Analysis of Japanese: the Verb Phrase Module (Yukiko Sasaki Alam) ...
Logical aspects of computational linguistics: An introduction
Logical Aspects of …, 1997
The papers in this collection are all devoted to single theme: logic and its applications in computational linguistics. They share many themes, goals and techniques, and any editorial classification is bound to highlight some connections at the expense of other. Nonetheless, we have found it useful to divide these papers (somewhat arbitrarily) into the following four categories: logical semantics of natural language, grammar and logic, mathematics with linguistic motivations, and computational perspectives. In this introduction, we use this four-way classification as a guide to the papers, and, more generally, to the research agenda that underlies them. We hope that the reader will find it a useful starting point to the collection.
Insight to Computational Linguistics
The field of computational linguistics (CL), together with its engineering area of natural language processing (NLP), has burst out in recent years. It has emerged rapidly from a relatively unclear accessory of both AI and formal linguistics into a blooming scientific discipline. It has also become an important area of business development. The focus of research in CL and NLP has shifted over the past three decades from the study of small prototypes and theoretical models to robust learning and processing systems applied to large corpora [1]. For the last two centuries, human race has effectively coped with the computerization of many tasks using automatic and electrical devices, and these devices realistically help people in their everyday life. In the second half of the twentieth century, human consideration has turned to the automation of natural language processing. Community now wants assistance not only in automatic, but also in rational efforts. They would like the machine to read an unwary text, to test it for correctness, to carry out the instructions contained in the text, or even to realize it well enough to produce a reasonable reply based on its meaning. Intelligent natural language processing is based on the science called computational linguistics. Computational linguistics is closely connected with applied linguistics and linguistics in general [2].This paper intends to provide an introduction to the major areas of CL, and an impression of current work in this area.