Natural language processing as a source of linguistic knowledge

The paper discusses a number of specific problems of natural text parsing that emerge during the operation of a highly developed rule-based machine translation system, ETAP-3. Emphasis is laid on two classes of problems: 1) adequacy of linguistic description of the working languages of the MT system and 2) means of resolving lexical and syntactic ambiguity of the source text. It is claimed that no parser, however sophisticated or advanced, can be made entirely free of lacunae and gaps. The reason is that many of the linguistic facts, including those critical for parser operation, have never come into view of researchers simply because they have not had at their disposal mass material of unexpected or incorrect parsing. It is exactly such material that is amply provided by a highly developed NLP system. If handled properly, this feedback helps the researcher to find the gaps of scientific descriptions and eliminate them. Consequently, linguistic experimentation with NLP systems becomes a rightful and very promising scientific method In a way, linguistic applications start to stimulate theoretical research, thus inverting the situation that has existed ever since NLP came to life.