Ilm-fan taraqqiyotida zamonaviy metodlarning qo‘llanilishi


ILM-FAN TARAQQIYOTIDA ZAMONAVIY METODLARNING QO‘LLANILISHI



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ILM FAN TARAQQIYOTIDA ZAMONAVIY METODLARNING QO‘LLANILISHI 15 son

ILM-FAN TARAQQIYOTIDA ZAMONAVIY METODLARNING QO‘LLANILISHI 
27.9.2021 
www.academics.uz 
51 
5. Masharipov Q, H.Rahimova. The castle is a castle. Urgench.2016 
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Sh.Vohidov.T.2010. 
7. Matrasulov Sh. Archaeological Survey Report of Qalajik Castle 2002 
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Xorezma.M., 1969 
9. Setmamatov M.B. Repair and beautification of Kalajik fortress, which echoes the 
history of Khorezm // Bulletin of Khorezm Mamun Academy. Khiva .2020. №8.6-
14.b 
10. Tolstov S.P. Ancient Khorezm. History and archaeological research. M. MGU. 
1948 
11. Khodjaniyazov G. Ancient Khorezm defense structures (from VI century BC to 
IV century AD). Tashkent. Uzbekistan. 2007. 
12. Gulomov Ya.G. History of Khorezm irrigation. From ancient times to the 
present. Tashkent, "Publishing House of the Academy of Sciences of Uzbekistan". 
1959 


ILM-FAN TARAQQIYOTIDA ZAMONAVIY METODLARNING QO‘LLANILISHI 
27.9.2021 
www.academics.uz 
52 
MORPHOLOGICAL AND SEMANTIC ANALYSIS OF HYPHENATED 
WORDS IN ENGLISH 
Shamsiyeva Nigora Zairovna 
Uzbekistan State University of World Languages 
Annotation:
Hyphenation is used when words form a unit, which reduces the 
possibility of parsing them into separate units or other forms. Much like sentences 
are composed of words, words themselves are composed of smaller units. For 
example, the English word questionably can be analyzed as question+able+ly. 
However, this structural decomposition of the word does not directly give us a 
semantic representation of the word’s meaning. Since morphology obeys the 
principle of compositionality, the semantics of the word can be systematically 
derived from the meaning of its parts. In this work, we propose a novel probabilistic 
model of word formation that captures both the analysis of a word w into its 
constituent segments and the synthesis of the meaning of w from the meanings of 
those segments. Our model jointly learns to segment words into morphemes and
compose distributional semantic vectors of those morphemes 

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