Difference between revisions of "User:Deltamachine"
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<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> |
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<p>'''Projects and experience:''' http://github.com/deltamachine</p> |
<p>'''Projects and experience:''' http://github.com/deltamachine</p> |
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| + | == Coding challenge == |
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| + | <p>https://github.com/deltamachine/wannabe_hackerman</p> |
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| + | <ul> |
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| + | <li><i>apertium_challenge1:</i> Write a script that takes a dependency treebank in UD format and "flattens" it, that is, applies the following transformations:</li> |
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| + | <ul><li>Words with the @conj relation take the label of their head</li> |
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| + | <li>Words with the @parataxis relation take the label of their head</li></ul> |
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| + | <p></p> |
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| + | <li><i>apertium_challenge2: </i>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. |
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| + | </li> |
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| + | </ul> |
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Revision as of 22:20, 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
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.
