Inferencing On Linguistically Based Semantic Structures

The paper characterizes natural language inferencing in the TIBAQ method of question-answering, focussing on three aspects: ~i) specification of the structures on which the inference rules operate, (ii) classification of the rules that have been formulated and implemented up to now, according to the kind of modification of the input structure ti~e rules invoke, an~ (iii) discussion of some points in which a proverly designed inference procedure may help the searc~ of the answer, and vice versa. I SPECIFICATION OF THE I:~PUT STRUCTURES FOR INFE~ENC I[IG A. Outline of the TIBAQ ~lethod hhen the TIBA~ (~ext-and-~nference based ~nswering of ~uestions) project was ~esigned, main emphasis was laid on the automatic build-up of the stock of knowledge from the (non-~re-edited% input text. The experimental system based 6n this method converses automatically the natural language input (both the questions and new Fieces of information, i.e. Czech sentences in their usual form) into the reDresentations of n,eaning (tectogranmlatical representations, TR's]; these TR's serve as input structures for the inference procedure tilat enriches the set of TR's selected by the system itself as possibly relevant for an answer to the input question. In this enriched set suitable TR's for direct and indirect answers to the given question are retrieved, and then transfered by a synthesis procedure into the output (surface) form if sentences (for an outline of the method as such, see Haji~ov~, 197~; 3aji~ov~ and Sgall, 19~i; Sgall, 1982). B. :?hat Kind of Structure Inferences ~houl~i Be Based on To decide what kind of structures the inference procedure should operate, one has to take into account several criteria, some of which seemingly contradict each other: the structures should be as simple and transparent as possible, so that inferencing can be perfor,ued in a well-defined way, and at the s~e ti~ue, these structures ~hould be as"exDressive"as the natural language sentences are, not to lose any piece of information captured by the text. "~atural language has a major drawback in its ambiguity: when a listener is told that the criticisl~ of the Polish delegate was fully justified, one does not know (unless indicated by the context or situation) whether s/he should infer that soE~eone criticized the Polish delegate, or whether the Polish delegate criticized someone/something. On the other hand, there are means in natural language that are not preserved by most languages that logicians have used for drawing consequences, but that are critical for the latter to be drawn correctly: when a listener is told that ~ussiau is ~poken in SIBERIA, s/he draws conclusions partly different from those when s/he is told that in Siberla, RUS3IAN is spoken (caoitals denoting the intonation center); or, to borrow one of the widely discussed examples in linguistic writings, if one hears that Jonn called :ary a ~U~LICA~ and that then she insulted I~IM, one should infer that the sneaker considers "being a ~eoublican" an insult~ this is not the case, if the speaker said that then she I~SULTED hi~. These and similar considerations have led the authors of TIDAn to a stronc conviction that the structures representing F.nowledge and serving as the base for inferencing in a q-uestion-answerin[~ system with a natural language interface should be linguistically based: they should be deprived of all ambiguities of natural language and at the same til:ie they should preserve all the information relevant for drawing conclusions that the natural lanciuage sentences encompass. The exr.erir,~ental syster~, based on TI~A(:, which was carried out by the group of formal linauistics at Charles University, Prague [implemented on ~C 1040 c~:n?11ter, compatible with 15::4 360) works with representations of :~eaning (tectogrammatical representations, fR's2 worked nut in the framework of functional generahive descrintion, or ~GD (for the linguistic background of this aopro~ch we refer to Sgall, 1964; ~;~all et ai.,1959;