Computational Linguistics in the Netherlands 2001

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.

2003

This collection of invited papers covers a lot of ground in its nearly 800 pages, so any review of reasonable length will necessarily be selective. However, there are a number of features that make the book as a whole a comparatively easy and thoroughly rewarding read. Multiauthor compendia of this kind are often disjointed, with very little uniformity from chapter to chapter in terms of breadth, depth, and format. Such is not the case here. Breadth and depth of treatment are surprisingly consistent, with coherent formats that often include both a little history of the field and some thoughts about the future. The volume has a very logical structure in which the chapters flow and follow on from each other in an orderly fashion. There are also many crossreferences between chapters, which allow the authors to build upon the foundation of one another's work and eliminate redundancies.

aclweb.org

1996

1987

We describe a methodology and associated software system for the construction of a large lexicon from an existing machine-readable (published) dictionary. The lexicon serves as a component of an English morphological and syntactic analyesr and contains entries with grammatical definitions compatible with the word and sentence grammar employed by the analyser. We describe a software system with two integrated components. One of these is capable of extracting syntactically rich, theory-neutral lexical templates from a suitable machine-readabh source. The second supports interactive and semi-automatic generation and testing of target lexical entries in order to derive a sizeable, accurate and consistent lexicon from the source dictionary which contains partial (and occasionally inaccurate) information. Finally, we evaluate the utility of the Longman Dictionary of Contemporary EnglgsA as a suitable source dictionary for the target lexicon.

2010

s of the Israeli Seminar on Computational Linguistics Wednesday, 16 June 2010

2000

International Journal of Advances in Scientific Research and Engineering (ijasre), 2024

Linguistics is concerned with rules that are followed by languages as a system. Computational linguistics (CL) combines the power of machine learning and human language. As a subfield of linguistics, CL is concerned with the computational description of rules that languages follow. It is what powers anything in a machine or device that has to do with language—speaking, writing, reading, and listening. It is often linked with natural language processing (NLP), which is the use of computers to identify structures in natural language. The boundary between NLP and CL is not so clear-cut. This paper is a primer on computational linguistics.

In this paper, we overview the ways in which computational methods can serve the goals of analysis and theory development in linguistics, and encourage the reader to become involved in the emerging cyberinfrastructure for linguistics. We survey examples from diverse subfields of how computational methods are already being used, describe the current state of the art in cyberinfrastructure for linguistics, sketch a pie-in-the-sky view of where the field could go, and outline steps that linguists can take now to bring about better access to and use of linguistic data through cyberinfrastructure.

2005

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.

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

In this paper, we present an overview of recent advances in selected areas of computational linguistics. We discuss relation of traditional levels of language -phonetics/phonology, morphology, syntax, semantics, pragmatics, and discourse -to the areas of computational linguistics research. Then the discussion about the development of the systems of automatic morphological analysis is given. We present various morphological classifications of languages, discuss the models that are necessary for this type of systems, and then argue that an approach based on "analysis through generation" gives several advantages during development and the grammar models that are used. After this, we discuss some popular application areas like information retrieval, question answering, text summarization and text generation. Finally, usage of graph methods in computational linguistics is dealt with.

2008

Recent years have shown an increased interest in bringing the field of graph theory into Natural Language Processing. In many NLP applications entities can be naturally represented as nodes in a graph and relations between them can be represented as edges.