Difference between revisions of "User:Deltamachine"

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<li>Language Diversity and Typology</li>
<li>Language Diversity and Typology</li>
<li>Introduction to Data Analysis</li>
<li>Introduction to Data Analysis</li>
<li>Discrete Math</li>
<li>Math (Discrete Math, Linear Algebra and Calculus, Probability Theory and Mathematical Statistics)</li>
<li>Linear Algebra and Calculus</li>
<li>Probability Theory and Mathematical Statistics</li>
</ul>
</ul>
<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>

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

VK: http://vk.com/anya_archer

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.