Paradigmatic definitions

The introduction and naming of these additional entities provides us with the opportunity to set the examples above in relation to other definitions associated with the terms computational linguistics and natural language processing. In this case, the definitions have been formulated

from a more paradigmatic point of view14. They were taken from the systematic dictionary of corpus linguistics (2013):

Computational Linguistics (CL) A branch of linguistics in which computa- tional techniques and concepts are applied to the elucidation of linguistic and pho- netic problems. Several research areas have developed, including speech synthesis, corpus linguistics, speech recognition,machine translation, concordance compilation,

testing of grammars, and many other areas where statistical counts and analyses are required.

See Newmeyer 1988: Ch. 11; McEnery 1992; Souter and Atwell 1993.

Apparently, the definition emphasizes the perspective of processing language, since the encod- ing of “linguistic and phonemic problems” is imperative for solving them by simulation. We

can interpret computational linguistics therefore as an area that is concerned with the opera-

tionalization of linguistic theories in computer models or the “translation” between theories and

14_{The “Systematic Dictionary of Corpus Linguistics is an attempt to group, systemize, define and explain the}

basic English terms in Corpus linguistics and relative fields”, Centre of Computational Linguistics, 2013. The dictionary, or rather glossary, was obviously compiled by extracting and summarizing salient properties from specialized literature, which highlights again the function of dictionaries as discourse (1).

models. This overlaps the idea of “processing language” (Example 5.3), even ifprocessing there

entails the processing of models of the language system, rather than the processing of discourse

(Example 5.5). “Discourse processing” is however exactly what is suggested by the enumeration of “speech recognition, [...] corpus linguistics, concordance compilation”. The definition presents a picture as confusing as that which emerges from the discourse examples insofar as it is now the field of computational linguistics that is said to overlie natural language processing in the area

ofmachine translation and corpus linguistics:

Natural Language processing (NLP) A general term used to refer to all pro-

cesses related to analysis of texts in natural languages (natural language - imitation of a human language by a machine) as well as their understanding and synthesis with human language. Natural Language Processing is closely related to the other fields of Computational Linguistics: Machine Translation (MT), Artificial Intelligence (AI), Corpus Linguistics.

See Sparck Jones 1992; Galliers and Sparck Jones 1993; Rustin 1973.

Furthermore, the hierarchy of fields suggested here is problematic, as it is not clear whether or not

natural language processingshould be seen as a sub-ordinate (as in the definition ofcomputational linguistics, which considers CLa branch of “linguistics”) or a co-ordinate field of computational linguistics (this is at least implied in Example 5.3), or whether we should consider all of these

terms to denote in fact the same area of activity (Example 5.2). The latter interpretation could be supported by aprototype view15 based on the observedrepetition of the termscorpus linguistics

andmachine translation as parts of the extension of either definition. This also recurs implicitly

in a third one, in this case relating tolanguage engineering:

Language Engineering (LE) The aim of Language Engineering (or sometimes can be referred to as language technology) is to facilitate the use of telematics ap- plications and to increase the possibilities for communication in and between world languages by integrating new spoken and written language processing methods. Lan-

guage Engineering covers the following action lines: (i) creation and improvement of pilot applications (document creation and management, information and com- munication services, translation and foreign language acquisition); (ii) corpora; (iii)

language engineering research; (iv) support issues specific to language engineering (i.e. standards, assessment and evaluation, awareness activities, user surveys).

See Andersen 1995; Cohen et al. 1990.

As noted, the idea ofcorporaappears again (concordance compilation,corpus linguistics), whereas

the idea of machine translation can be seen as implicit in “telematics applications” which “in- crease the possibilities for communication in and between world languages by integrating new spoken and written language processing methods [... and] translation”. As if to further confound

the analyst,language engineering is here regarded as asynonymforlanguage technology, contrary

to Wright’s (2002) distinction.

However, we find the notion of the “creation and improvement of [...] applications [for] docu- ment creation and management” cogent with the argument of Example 5.1. The idea of “aware- ness activities” which suggests that people should be coaxed intoadapting to those applications

and so be “engineered” to use language – especially terminology – in ways that are more sup- portive of theprocedures oflanguage engineering is implicit in Example 5.5.

15

In this case, we can categorize the definition of computational linguistics as expressing anumbrella category, and that ofnatural language processing as expressing anactivity-related category under its umbrella.

All things considered, the experiment has not so much facilitated the selection of procedures

from language engineering than provided perturbations to accommodate an idea that will be

presented as our reinterpretation of the concept. Before we present it, we will make a final attempt at constructing an interpretation that is perhaps best described as a variant offormal concept analysis (1.2.4.1).