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<Paper uid="C90-3058">
  <Title>A LARGE RUSSIAN MORPHOLOGICAL VOCABULARY FOR IBM COMPATIBLE~, AND METHODS OF ITS COMPRESSION</Title>
  <Section position="1" start_page="0" end_page="0" type="metho">
    <SectionTitle>
A LARGE RUSSIAN MORPHOLOGICAL VOCABULARY
FOR IBM COMPATIBLE~,
AND METHODS OF ITS COMPRESSION
Igor A. BOLSHAKOV
</SectionTitle>
    <Paragraph position="0"> VINZTI, Academy' of Sciences of USSR Moscow 125219, Bal-t;iyskayE~ u\].. 14, USSR There are only few Russian vocabularies in computerized form in the USSR new, so development of a new Russian vocabulary large enough for spell checking is still topical.</Paragraph>
    <Paragraph position="1"> The requirements for such a vocabulary are at least as follows : l} sore than iO0,O00 lexemes included; 2) modern and diversified lexicon well covering the sciences, many technological fields, the humanities, and may be the everyday life; 3) mapping the most of numerous lexeme forms implied by the ~lectional nature of Russian, and at the same time acceptance of well-formed words only; 4) orientation to IBM-compatible PCs most commonly used in the USSR ~owaday.</Paragraph>
    <Paragraph position="2"> Such a w)oabulary has been recently built by the a~thor. Its parameters are as follows: 67,400 stems covering more than 104,700 Russian lexemes and their ~.425 million word-forms (i,e. 21.2 forms/stem); the ~isimal, the mean, and the maximal stem lengths amounting to 1, 7.8, and 32 letters accordingly; the textual form size being about 865 KB, Our morphologic~l ciassific~tlo~ of stems is quite original and deals not only with ~ord formation, but also with word derivation. The scheme includes i18 classes and 1901 various fieetions (variable suffixal chains). Separate classes were introduced among mentioned ones for invariant words, irregular forms, and abbreviations, The first 38 classes cover more than 83X of all stems.</Paragraph>
    <Paragraph position="3"> The split borders of stems were freely moved to the left while classifying, if morphological alternations or identical final letters in a whole stem class have been encountered. The shortest flection is an empty one, the longest flections include up to 12 letters (e.g. HPOMB~WHC~), so the m~an fleetion length grew up to 6 letters, which is comparable to the mean stem length.</Paragraph>
    <Paragraph position="4"> The textual form of vocabularies is not convenient for applications and has to be transformed into binary working form. The wellknown arehivization packages such as PKA~q/PKXARC are not acceptable for this perpose because of low squeeze ratio and uselessness of the arehivized form as a working one for spellers or any other application, So several other methods of compression were analyzed, Basically the Huffman method has been selected for coding morphological class numbers, and the Cooper method has been picked up for the stems, Additionally the RADIX-50 method was applied to both of the components of a vocabulary entry.</Paragraph>
    <Paragraph position="5"> Several other techniques are turned out to be useful for additional stem compression in large vocabularies. They are based on l} frequent recurrences of differently classified, but literally identical stess; 2) coamoness of events in nearly saturated vocabularies, when the first letter in the deflecting part of a stem is alphabetically adjacent to the letter in the same position within previous stem; 3) availability of several free positions in RADIX-50 code table (only 33 of gO are grasped by Eussian letters and a delimiter). These unoccupied values night be used for re-coding final stem letters, digrams, and trigrams most frequent in different stem classe~, This technique squeezes the letter part of a vocabulary entry and make the delimiter preceding the next entry unnecessary.</Paragraph>
    <Paragraph position="6"> All methods mentioned were investigated, separately and in combinations. The Huffman's + the Cooper's + RADIX-50 combination has given us a sqeeze ratio about 3.4, whereas addition of the rest techniques has incremented the ratio up to 4.2 - 4.5. So only about l~O KB in memory is needed for this working form, which is easy allocatable as a resident part of a modern text processor, As compared te vocabularies in available English language spellers, the size achieved seems to be highly competitive in our more complex inflectional case.</Paragraph>
    <Paragraph position="7"> The vocabulary is available beth in the textual and in binary forms, Several utilities concerned with its compiling, debugging, and squeezing are ready too. The utilities were written using Turbo Pascal 5.0 and Turbo Professional packages and are wholly applicable for processing any other natural language vocabulary,</Paragraph>
  </Section>
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