Difference between revisions of "User:Deltamachine"
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<li>Language Diversity and Typology</li> |
<li>Language Diversity and Typology</li> |
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<li>Introduction to Data Analysis</li> |
<li>Introduction to Data Analysis</li> |
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| − | <li>Discrete Math</li> |
+ | <li>Math (Discrete Math, Linear Algebra and Calculus, Probability Theory and Mathematical Statistics)</li> |
| − | <li>Linear Algebra and Calculus</li> |
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| − | <li>Probability Theory and Mathematical Statistics</li> |
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</ul> |
</ul> |
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<p>'''Technical scills:''' Python (experienced, 1.5 years), HTML, CSS, Flask, Django, SQLite (familiar)</p> |
<p>'''Technical scills:''' Python (experienced, 1.5 years), HTML, CSS, Flask, Django, SQLite (familiar)</p> |
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Revision as of 13:02, 9 March 2017
Contact info
Name: Anna Kondratjeva
Location: Moscow, Russia
E-mail: an-an-kondratjeva@yandex.ru
Phone number: +79250374221
Github: http://github.com/deltamachine
IRC: deltamachine
Timezone: UTC+3
About me
Education: Bachelor's Degree in Fundamental and Computational Linguistics (2015 - expected 2019), National Research University «Higher School of Economics» (NRU HSE)
Main university courses:
- Programming (Python)
- Computer Tools for Linguistic Research
- Theory of Language (Phonetics, Morphology, Syntax, Semantics)
- Language Diversity and Typology
- Introduction to Data Analysis
- Math (Discrete Math, Linear Algebra and Calculus, Probability Theory and Mathematical Statistics)
Technical scills: Python (experienced, 1.5 years), HTML, CSS, Flask, Django, SQLite (familiar)
Projects and experience: http://github.com/deltamachine
Languages: Russian, English, German
Coding challenge
https://github.com/deltamachine/wannabe_hackerman
- apertium_challenge1: Write a script that takes a dependency treebank in UD format and "flattens" it, that is, applies the following transformations:
- Words with the @conj relation take the label of their head
- Words with the @parataxis relation take the label of their head
- apertium_challenge2: Write a script that takes a sentence in Apertium stream format and for each surface form applies the most frequent label from the labelled corpus.
