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A.A. Protskevich CALCULATION OF OPTIMAL PARAMETERS OF THE CODE DICTIONARY
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- I.A.Purtov
A.A. Protskevich CALCULATION OF OPTIMAL PARAMETERS OF THE CODE DICTIONARYThe problem of effective representation and processing of the textual information in network information systems remains urgent, despite of significant achievements in this direction. Within the bounds of this problem the textual information compression, coding and transfer tasks oriented on use in distributed information environment are of particular importance. The textual information coding efficiency can be increased by integration of the information code block from a symbol up to a word. At a stage of analysis of a sentence, we receive a set of stems and morphological codes, which can be used to code the input text. The creation and application of the code dictionary of stems and morphemes for the morphological analysis and coding of the textual message is the basis for the given method. Let's calculate potential compression factor of the textual message depending on code dictionary size under condition of unlimited size of the message dictionary. Then the compression depending on code dictionary size is determined as:
where k is a compression factor; nD is code dictionary size; p(i) is probabilities of a ith word occurrence, provided that words are sorted in decreasing order probabilities of occurrence determined by the generalized Zipf-Mandelbrot's law taking into account deviations in the low-frequency zone; hD(nD,i) and h(i) are costs in bits of the coded and not coded word, depending on particular language and the index construction method in the code dictionary; n(N) is code dictionary size on a excerption of size N; C and L is amount of symbols in the alphabet and average length of a word in a natural language. Using the fact of various frequencies of words in natural language, prefix codes can be applied for index construction in the code dictionary to improve the compression factor. The carried out calculations showed that with large nD the textual information coding efficiency increases with growth of the code dictionary size. For Russian the compression achieves 37% and 16% with usage the prefix coding for index construction in the code dictionary. Therefore, there is no sense to limit the code dictionary size. I.A.PurtovMODELING OF SECOND ORDER RELATIONS WITH THE HELP OF THE LANGUAGE PROLOGConsidering knowledge representation which is present in texts of a certain requirements, specifications and QA-protocols, it’s necessary for us to introduce relations of a higher level than the predicate(subject, object) or the property(thing). It’s due to a more complicated semantic structure of real events described by sentences of a natural language. Such time a spatial relations as “earlier”, “later”, “at the same time”, “closer”, “further”, “lower”, “higher” and so on are good examples of the constructions like that.
However, it’s quite possible to make such predicates with the help of means of the standard Prolog. While working at the problem the researcher has got typical constructions (a sequences of predicates) that imitate second order relations for different combinations of the names of subject, object and predicate relations. Among these relations there’s a common case when the names of subjects and objects of connecting predicates are different; the case when predicates have the same names of subjects and objects; and, lastly, the case of the relations between the same predicates. A short Prolog-program can serve as a good example (see p.1). Here, the predicates pred1 and pred2 are some relations between certain subjects of the relations (subj1 and subj2) and objects (obj1 and obj2). The relation rel functioning as a second order predicate, connects first-order constructions. Of course, in reality it’s not a second order predicate because it doesn’t connect predicates themselves but it does their names. Besides, as mentioned above, the relation rel is the relation between symbol chains (‘symbol’). Nevertheless, the model like that is adequate enough because it fulfils the requirements of truth made towards second order relations, that is:
predicates pred1(symbol, symbol) pred2(symbol, symbol) rel(symbol, symbol) clauses pred1(subj1, obj1). pred2(subj2, obj2). rel(pred1, pred2) :- pred1(subj1, obj1), pred2(subj2, obj2). p.1. Modeling of second order relations in the Prolog. The example mentioned above is valid on condition that the predicates shouldn’t have the same names and arguments, that is, the relation exists between two different actions made by different subjects over different objects. It happens very often when a second order relation connects the events that have the same subject and object. In this case it’s necessary to introduce pseudonyms – single-seat predicates that takes the same meaning as the starting predicate. Directory: confer confer -> Tilte : a critical examination of the police relations with bbc confer -> Eavesdropping on a virtuous circle Richard Whately and the Oriel Noetics. Elena Pasquini Douglas uwa business School confer -> Simulation and Prediction of Storm Surges, Waves, and Morphological Changes due to Tropical Cyclones by Using a pc-based Integrated Coastal Process Model confer -> Panel 0511 Disability and Difference I: Post-War Journeys through Disability confer -> Do remittances have a flip side? A general equilibrium analysis of remittances, labor supply responses and policy options for Jamaica* Maurizio Bussolo and Denis Medvedev confer -> Conference approval process made easy for acm in cooperation conferences confer -> Proceedings of gt2009 asme turbo Expo 2009: Power for Land, Sea and Air Orlando, Florida, USA gt2009-59981 dynamics of premixed h2/CH4 flames under near blowoff conditions confer -> South Korea’s Economic Future: Industrial Policy, or Economic Democracy? confer -> Asset rotator Cuff Rehabilitation Course Faculty Dr. Spero Karas confer -> Acm word Template for sig site Download 1.06 Mb. Share with your friends:
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