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Processing multi-lingual business data

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Interfax - Dun & Bradstreet review of the approaches to processing multi-lingual information

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Processing multi-lingual business data

  1. 1. SALES RELAUNCH F&Q SESSION
  2. 2. Multi-lingual data processing The CIS and Georgia Olga Rink, director general
  3. 3. 3 Content Interfax - Dun & Bradstreet, Innovations in Multi-lingual context • Business environment • Main stages of processing multi-lingual business data o Naming convention o Transliteration o Matching • Seeding and verifying objects in a media coverage
  4. 4. 4 Official languages, population (mn) and Russian as a second language (est.) Interfax - Dun & Bradstreet, Innovations in Multi-lingual context
  5. 5. 5 Multi-lingual environment Interfax - Dun & Bradstreet, Innovations in Multi-lingual context Country Official language (group) Population, mn Alphabet Second language Russian, % of population, est. Russia Russian 150Cyrillic 35+* official and over 100 used  100% Armenia Armenian (Indo-European language) 3Own script Russian, English 100% Azerbaijan Azeri Turkish 9,8 Latin in Azerbaijan, Cyrillic in Russia (Dagestan) 90% Belarus Bielaruskaja mova, Russian 9,5Cyrillic Russian  100% Georgia Georgian (Kartvelian language) 3,7Georgian script Russian, English, Azeri, Armenian 100% Kazakhstan Kazakh (Turkic language), Russian 17,7 Kazakh alphabets (Cyrillic, Latin, Perso-Arabic, Kazakh Braille) Russian  100% Kyrgyzstan Kyrgyz (Turkic language), Russian 6Cyrillic Kyrgyz  100% Moldova Romanian 3,6Latin Russian is widely used  90% Tajikistan Tajik (Persian dialect) 8Cyrillic Russian 90% Turkmenistan Turkmen (Turkic language) 5,2Cyrillic, Latin Russian is used 100% Ukraine Ukrainian (Ukrayins'ka mova) 42,5Cyrillic Russian is widely used along with a number of other languages  100% Uzbekistan Uzbek, in fact Russian 31,6Cyrillic, Latin Russian is widely used 100% • The Constitution of Dagestan defines "Russian and the languages of the peoples of Dagestan" as the state languages •  a bulk of newly-registered business is available in Cyrillic or Latin
  6. 6. 6Interfax - Dun & Bradstreet, Innovations in Multi-lingual context • For Slavic languages we use ISO 9:1995 standard with one exception: put a combination of Latin characters instead of Latin diacritic characters. Example: Ch (without diacritic) instead of Ч – Č (with diacritic) • ISO9985 is used for Armenian • ISO 9984 – for Georgian • ООО «Ъ» (Trade style: OOO TVERDY ZNAK; OOO “” is a transliterated name – no way to find by the original name) • Minor changes in transliteration like 3DNYUS, OOO >3DNEWS, LLC are accepted and now filtered while being updated • Matching rules are defined in our “Naming Convention”: i.e. the transliterated «normalized» Charter brief company name is used as primary: an indication to a legal form in the name (required by law) is put at the end via comma. • Second one is the transliterated full legal name. • Trade style contains official name in English/Latin or trade marks • We use rule-based and machine learning approaches, including areas of collecting data, identifying objects, developing credit scorings, digesting media coverage
  7. 7. 7 Natural Language Processing and Machine Learning The SCAN engine is leveraging vast amounts of text data to enable the next generation of Interfax data products Interfax - Dun & Bradstreet, Innovations in Multi-lingual context Interfax builds a scalable machine learning infrastructure that enables data scientists and engineers to explore, train, and deploy credit and reputation risk models with minimal effort • Tagging documents and • Classifying by a text type (media-release, forecast, feature etc) Detecting and Disambiguating Named Entities Support Vector Machine (SVM) or Bayes are used, depending on configuration • SVM represents a text as a vector to compare with a pattern (prototype); The closeness defines the type • Bayes rule is applicable when you rely on pre-determined assumptions (a range of known “symptoms”) while calculating probabilities Rule-based fact extraction and sentiment analysis At an initial phase for seeding named persons • Rule-based approach mostly • Context analysis and statistics for entity disambiguation Clarification of Named Entity Detection with learning semi- automatically labelled corpus • Support Vector Machine (SVM) • A neural network on the basis of the existing rule-based structure is considered for future
  8. 8. 8 An intellectual WOW-effect or what can only SCAN do – forward to “verifying” media coverage Interfax - Dun & Bradstreet, Innovations in Multi-lingual context Out of 3 mn companies automatically generated by the Scan linguistic kernel for the recent year 22 thousand have been verified, 0.5 mn are identified with Spark 2 mn persons were generated (seeded); out of them 75 thousand verified 300 thousand of geographic locations: all Russian ones identified by OKATO classifier and many global locations got by parsing Wikipedia 13 thousand trade marks (“Trade style”) 24 thousand sources in Russian
  9. 9. ThankYou Interfax – Dun & Bradstreet www.dnb.ru

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