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== Contact information == |
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<p>'''Name:''' Anna Kondratjeva</p> |
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<p>'''Location:''' Moscow, Russia</p> |
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<p>'''E-mail:''' an-an-kondratjeva@yandex.ru</p> |
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<p>'''Phone number:''' +79250374221</p> |
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<p>'''Github:''' http://github.com/deltamachine</p> |
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<p>'''IRC:''' deltamachine</p> |
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<p>'''Timezone:''' UTC+3</p> |
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== Skills and experience == |
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<p>'''Education:''' Bachelor's Degree in Fundamental and Computational Linguistics (2015 - expected 2019), National Research University «Higher School of Economics» (NRU HSE)</p> |
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<p>'''Main university courses:'''</p> |
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<ul> |
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<li>Theory of Language (Phonetics, Morphology, Syntax, Semantics)</li> |
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<li>Programming (Python)</li> |
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<li>Computer Tools for Linguistic Research</li> |
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<li>Language Diversity and Typology</li> |
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<li>Introduction to Data Analysis</li> |
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<li>Math (Discrete Math, Linear Algebra and Calculus, Probability Theory and Mathematical Statistics)</li> |
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</ul> |
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<p>'''Technical skills:''' Python (experienced, 1.5 years), HTML, CSS, Flask, Django, SQLite (familiar)</p> |
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<p>'''Projects and experience:''' http://github.com/deltamachine</p> |
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<p>'''Languages:''' Russian (native), English, German</p> |
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== Why is it you are interested in machine translation? == |
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== Why is it that you are interested in Apertium? == |
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== 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 == |
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== A description of how and who it will benefit in society == |
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== Work plan == |
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=== Post application period === |
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<ul> |
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<li>Getting closer with Apertium, reading documentation, playing around with its tools</li> |
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<li>Setting up Linux and getting used to it</li> |
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<li>Learning more about UD treebanks</li> |
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<li>Learning more about machine learning</li> |
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</ul> |
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=== Community bonding period === |
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<ul> |
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<li>Choosing language pairs, with which shallow function labeller will work.</li> |
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<li>Choosing the most appropriate Python ML library (maybe it will be Tensorflow, maybe not)</li> |
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</ul> |
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=== Work period === |
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<ul> |
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<li>'''1st month:''' preparing the data, proceeding treebanks, creating datasets for training.</li> |
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<li>'''2nd month:''' working on a classifier, testing.</li> |
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<li>'''3rd month:''' integrating shallow function labeller to Apertium, testing, fixing bugs, writing documentation.</li> |
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</ul> |
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=== Schedule === |
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<ul> |
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<li>'''Week 1:'''</li> |
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<li>'''Week 2:'''</li> |
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<li>'''Week 3:'''</li> |
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<li>'''Week 4:'''</li> |
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<li>'''Deliverable #1, June 26 - 30:'''</li> |
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<li>'''Week 5:'''</li> |
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<li>'''Week 6:'''</li> |
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<li>'''Week 7:'''</li> |
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<li>'''Week 8:'''</li> |
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<li>'''Deliverable #2, July 24 - 28:'''</li> |
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<li>'''Week 9:'''</li> |
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<li>'''Week 10:'''</li> |
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<li>'''Week 11:'''</li> |
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<li>'''Week 12:'''</li> |
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<li>'''Project completed'''</li> |
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</ul> |
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== Non-Summer-of-Code plans you have for the Summer == |
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I have exams in the university till 3rd week of June, so I will be able to work only 20-25 hours per week. But I will try to pass as many exams as possible ahead of schedule, so it may be changed. |
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After that I will be able to work full time and spend 45-50 hours per week on the task. |
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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>''flatten_conllu.py:'' 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> |
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<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> |
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</ul> |
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<li>''calculate_accuracy_index.py:'' A script that does the following:</li> |
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<ul> |
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<li>Takes -train.conllu file and calculates the table: surface_form - label - frequency</li> |
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<li>Takes -dev.corpus and for each token assigns the most frequent label from the table</li> |
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<li>Calculates the accuracy index</li> |
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</ul> |
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<li>''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. |
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</li> |
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</ul> |
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