Difference between revisions of "User:Deltamachine/proposal"
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== Which of the published tasks are you interested in? What do you plan to do? == |
== Which of the published tasks are you interested in? What do you plan to do? == |
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I would like to implement a prototype shallow syntactic function labeller. |
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== Reasons why Google and Apertium should sponsor it == |
== Reasons why Google and Apertium should sponsor it == |
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Revision as of 06:38, 15 March 2017
Contents
- 1 Contact information
- 2 Skills and experience
- 3 Why is it you are interested in machine translation?
- 4 Why is it that you are interested in Apertium?
- 5 Which of the published tasks are you interested in? What do you plan to do?
- 6 Reasons why Google and Apertium should sponsor it
- 7 A description of how and who it will benefit in society
- 8 Work plan
- 9 Non-Summer-of-Code plans you have for the Summer
- 10 Coding challenge
Contact information
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
Skills and experience
Education: Bachelor's Degree in Fundamental and Computational Linguistics (2015 - expected 2019), National Research University «Higher School of Economics» (NRU HSE)
Main university courses:
- Theory of Language (Phonetics, Morphology, Syntax, Semantics)
- Programming (Python)
- Computer Tools for Linguistic Research
- Language Diversity and Typology
- Introduction to Data Analysis
- Math (Discrete Math, Linear Algebra and Calculus, Probability Theory and Mathematical Statistics)
Technical skills: Python (experienced, 1.5 years), HTML, CSS, Flask, Django, SQLite (familiar)
Projects and experience: http://github.com/deltamachine
Languages: Russian (native), English, German
Why is it you are interested in machine translation?
Why is it that you are interested in Apertium?
Which of the published tasks are you interested in? What do you plan to do?
I would like to implement a prototype shallow syntactic function labeller.
Reasons why Google and Apertium should sponsor it
A description of how and who it will benefit in society
Work plan
Post application period
Community bonding period
Work period
- Week 1:
- Week 2:
- Week 3:
- Week 4:
- Deliverable #1
- Week 5:
- Week 6:
- Week 7:
- Week 8:
- Deliverable #2
- Week 9:
- Week 10:
- Week 11:
- Week 12:
- Project completed
Non-Summer-of-Code plans you have for the Summer
Coding challenge
https://github.com/deltamachine/wannabe_hackerman
- flatten_conllu.py: 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
- calculate_accuracy_index.py: A script that does the following:
- Takes -train.conllu file and calculates the table: surface_form - label - frequency
- Takes -dev.corpus and for each token assigns the most frequent label from the table
- Calculates the accuracy index
- label_asf: A script that takes a sentence in Apertium stream format and for each surface form applies the most frequent label from the labelled corpus.
