caa a22 4500 465752284 CHVBK 20180323111837.0 cr unu---uuuuu 170327e19900601xx s 000 0 eng 10.1007/BF00393758 doi (NATIONALLICENCE)springer-10.1007/BF00393758 Providing machine tractable dictionary tools [Elektronische Daten] [Yorick Wilks, Dan Fass, Cheng-ming Guo, James McDonald, Tony Plate, Brian Slator] Machine readable dictionaries (Mrds) contain knowledge about language and the world essential for tasks in natural language processing (Nlp). However, this knowledge, collected and recorded by lexicographers for human readers, is not presented in a manner for Mrds to be used directly for Nlp tasks. What is badly needed are machine tractable dictionaries (Mtds): Mrds transformed into a format usable for Nlp. This paper discusses three different but related large-scale computational methods to transform Mrds into Mtds. The Mrd used is The Longman Dictionary of Contemporary English (Ldoce). The three methods differ in the amount of knowledge they start with and the kinds of knowledge they provide. All require some handcoding of initial information but are largely automatic. Method I, a statistical approach, uses the least handcoding. It generates "relatedness” networks for words in Ldoce and presents a method for doing partial word sense disambiguation. Method II employs the most handcoding because it develops and builds lexical entries for a very carefully controlled defining vocabulary of 2,000 word senses (1,000 words). The payoff is that the method will provide an Mtd containing highly structured semantic information. Method III requires the handcoding of a grammar and the semantic patterns used by its parser, but not the handcoding of any lexical material. This is because the method builds up lexical material from sources wholly within Ldoce. The information extracted is a set of sources of information, individually weak, but which can be combined to give a strong and determinate linguistic data base. Kluwer Academic Publishers, 1990 computational lexicography nationallicence machine tractable dictionaries nationallicence pathfinder networkds nationallicence preference semantics nationallicence semantic primitives nationallicence word-sense disambiguation nationallicence Wilks Yorick Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut Fass Dan Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut Guo Cheng-ming Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut McDonald James Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut Plate Tony Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut Slator Brian Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut Machine Translation Kluwer Academic Publishers 5/2(1990-06-01), 99-154 0922-6567 5:2<99 1990 5 10590 https://doi.org/10.1007/BF00393758 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00393758 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wilks Yorick Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 700 1- Fass Dan Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 700 1- Guo Cheng-ming Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 700 1- McDonald James Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 700 1- Plate Tony Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 700 1- Slator Brian Computing Research Laboratory, New Mexico State University, Las Cruces, USA aut NATIONALLICENCE 773 0- Machine Translation Kluwer Academic Publishers 5/2(1990-06-01), 99-154 0922-6567 5:2<99 1990 5 10590 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer