REDUCED MODELS

We can formulate the problem of selecting a good model for any specific linguistic application as follows.

A holistic model of the language facilitates describing the language as a whole system. However, when we concentrate on the objectives of a specific application system, we can select for our purposes only that level, or those levels, of the whole language description, which are relevant and sufficient for the specific objective. Thus, we can use a reduced model for algorithmization of a specific application.

Here are some examples of the adequate choice of such a reduced description.

· If we want to build an information retrieval system based on the use of keywords that differ from each other only by their invariant parts remaining after cutting off irrelevant suffixes and endings, then no linguistic levels are necessary. All words like México, mexicanos, mexicana, etc., can be equivalent for such a system. Other relevant groups can be gobierno, gobiernos, or ciudad, ciudades, etc. Thus, we can use a list containing only the initial substrings (i.e., stems or quasi-stems) like mexic-, gobierno-, ciudad-, etc. We also will instruct the program to ignore the case of letters. Our tasks can be solved by a simple search for these substrings in the text. Thus, linguistic knowledge is reduced here to the list of substrings mentioned above.

· If we want to consider in our system the wordforms dormí, duermo, durmió, etc., or será, es, fui, era, sido, etc. as equivalent keywords, then we must introduce the morphologic level of description. This gives us a method of how to automatically reduce all these wordforms to standard forms like dormir or ser.

· If we want to distinguish in our texts those occurrences of the string México that refer to the name of the city, from the occurrences that refer to name of the state or country, then we should introduce both morphologic and syntactic levels. Indeed, only word combinations or the broader contexts of the relevant words can help us to disambiguate such word occurrences.

· In a spell checker without limitations on available memory, we can store all wordforms in the computer dictionary. Nevertheless, if the memory is limited and the language is highly inflectional, like Spanish, French or Russian, we will have to use some morphologic representation (splitting words to stems and endings) for all the relevant wordforms.

· In grammar checkers, we should take morphologic and syntactic levels, in order to check the syntactic structures of all the sentences. The semantic level usually remains unnecessary.

· For translation from one natural language to another, rather distant, language, all the linguistic levels are necessary. However, for translation between two very similar languages, only morphologic and syntactic levels may be necessary. For the case of such very “isomorphic” languages as Spanish and Portuguese, the morphologic level alone may suffice.

· If we create a very simple system of understanding of sentences with a narrow subject area, a small dictionary, and a very strict order of words, we can reduce the dictionary to the set of strings reflecting initial parts of the words actually used in such texts and directly supply them with the semantic interpretations. In this way, we entirely avoid the morphologic and syntactic problems; only the textual and the semantic levels of representation are necessary.

· If we create a more robust system of text understanding, then we should take a full model of language plus a reasoning subsystem, for the complete semantic interpretation of the text.

However, to make a reasonable choice of any practical situation, we need to know the whole model.

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COMMON FEATURES OF MODERN MODELS OF LANGUAGE	\|	DO WE REALLY NEED LINGUISTIC MODELS?

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