Difference between revisions of "User:Deltamachine"
Jump to navigation
Jump to search
Deltamachine (talk | contribs) |
Deltamachine (talk | contribs) |
||
| Line 23: | Line 23: | ||
<p>'''Technical scills:''' Python (experienced, 1.5 year), HTML, CSS, Flask, Django, SQLite (familiar)</p> |
<p>'''Technical scills:''' Python (experienced, 1.5 year), HTML, CSS, Flask, Django, SQLite (familiar)</p> |
||
<p>'''Projects and experience:''' http://github.com/deltamachine</p> |
<p>'''Projects and experience:''' http://github.com/deltamachine</p> |
||
| + | <p>'''Languages:''' Russian (native), English (B2/C1), German (A2)</p> |
||
== Coding challenge == |
== Coding challenge == |
||
Revision as of 22:21, 6 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
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
- Discrete Math
- Linear Algebra and Calculus
- Probability Theoty and Mathematical Statistics
Technical scills: Python (experienced, 1.5 year), HTML, CSS, Flask, Django, SQLite (familiar)
Projects and experience: http://github.com/deltamachine
Languages: Russian (native), English (B2/C1), German (A2)
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.
