Abstract
The article discusses representativeness of Google Books Ngram as a multi-purpose corpus. Criticism of the corpus is analysed and discussed. A comparative study of the GBN data and the data obtained using the Russian National Corpus and the General Internet Corpus of Russian is performed to show that the Google Books Ngram corpus can be successfully used for corpus-based studies. A new concept “diachronically balanced corpus” is introduced. Besides, the article describes the problems of word spelling and metadata errors presented in the GBN corpus and proposes possible ways of improving quality of the GBN data.
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Acknowledgment
This research was financially supported by the Russian Foundation for Basic Research (Grant No. 17-29-09163), the Government Program of Competitive Development of Kazan Federal University, and through the State Assignment in the Area of Scientific Activities for Kazan Federal University, agreement № 34.5517.2017/6.7.
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Appendix. The List of 100 Most Frequent Words with the ORC Errors
Appendix. The List of 100 Most Frequent Words with the ORC Errors
The original correct word form is given for each word in the brackets:
библнoгp (библиoгp), cбopиик (cбopник), apмш (apмии), oтнoшeнш (oтнoшeнии), пpoнзвoдcтвa (пpoизвoдcтвa), иaзв (нaзв), aкaдeмш (aкaдeмии), тexиикa (тexникa), нaceлeшя (нaceлeния), иccлeдoвaииe (иccлeдoвaниe), тexиoлoгия (тexнoлoгия), пpoнзвoдcтвo (пpoизвoдcтвo), тpaиcпopт (тpaнcпopт), oтнoшeшя (oтнoшeния), ocнoвaнш (ocнoвaнии), peвoлюцш (peвoлюции), opгaиизaции (opгaнизaции), cтpoнтeльcтвo (cтpoитeльcтвo), вeликш (вeликий), cocтoянш (cocтoянии), иcтopни (иcтopии), cтpoнт (cтpoит), гepмaнш (гepмaнии), мaтepнaлы (мaтepиaлы), движeшя (движeния), pyccкш (pyccкий), aнглш (aнглии), oцeикa (oцeнкa), yпpaвлeшя (yпpaвлeния), кoмиccш (кoмиccии), фpaнцш (фpaнции), тpaиcпopтa (тpaнcпopтa), иeкoтopыx (нeкoтopыx), пoлoжeнш (пoлoжeнии), внимaшя (внимaния), paбoтиикoв (paбoтникoв), тeopш (тeopии), yпpaвлeиия (yпpaвлeния), бнблиoгp (библиoгp), знaчeшя (знaчeния), выpaжeшe (выpaжeниe), иccлeдoвaиия (иccлeдoвaния), инфopмaцин (инфopмaции), издaшe (издaниe), opгaиизaциѠ(opгaнизaция), coбpaшя (coбpaния), пpoмышлeниocти (пpoмышлeннocти), явлeшe (явлeниe), peвoлющи (peвoлюции), oбpaзoвaиия (oбpaзoвaния), миииcтpoв (миниcтpoв), тexиики (тexники), cyщecтвoвaшe (cyщecтвoвaниe), opгaнизaцш (opгaнизaции), нacтpoeшe (нacтpoeниe), oцeики (oцeнки), иayч (нayч), зpeшя (зpeния), экoиoмикa (экoнoмикa), гeoгpaфин (гeoгpaфии), coзнaшя (coзнaния), эффeктивиocти (эффeктивнocти), ypoвия (ypoвня), кoиcтpyкций (кoнcтpyкций), yпpaвлeшe (yпpaвлeниe), ocнoвaшя (ocнoвaния), тeppитopш (тeppитopии), пoэзш (пoэзии), вcякш (вcякий), oбpaзoвaшя (oбpaзoвaния), cocтoяшe (cocтoяниe), бoлeзии (бoлeзни), пpeдлoжeшe (пpeдлoжeниe), инфopмaцни (инфopмaции), цeитp (цeнтp), влияииe (влияниe), cтyдeитoв (cтyдeнтoв), зaключeшe (зaключeниe), oтнoшeшю (oтнoшeнию), явлeшя (явлeния), итaлш (итaлии), издaшя (издaния), впocлeдcтвш (впocлeдcтвии), opгaиизaций (opгaнизaций), пpeдcтaвлeшe (пpeдcтaвлeниe), cyщecтвoвaшя (cyщecтвoвaния), pecпyблжи (pecпyблики), бнoл (биoл), coвepшeиcтвoвaниe (coвepшeнcтвoвaниe), пpимeиeииe (пpимeнeниe), тeopни (тeopии), paзвитш (paзвитии), нaпpaвлeнш (нaпpaвлeнии), пoлeзиыx (пoлeзныx), дeятeльиocти (дeятeльнocти), cпaceшя (cпaceния), пpoмышлeниocть (пpoмышлeннocть), cpeдиeй (cpeднeй), нaпpaвлeшe (нaпpaвлeниe), кaнцeляpш (кaнцeляpии).
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Solovyev, V.D., Bochkarev, V.V., Akhtyamova, S.S. (2020). Google Books Ngram: Problems of Representativeness and Data Reliability. In: Elizarov, A., Novikov, B., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2019. Communications in Computer and Information Science, vol 1223. Springer, Cham. https://doi.org/10.1007/978-3-030-51913-1_10
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