User:AMR-KELEG/GSoC19 Proposal
Contents
Personal Information
- Name: Amr Keleg
- E-mail address: amr.keleg@eng.asu.edu.eg / amr_mohamed@live.com
- IRC: AMR-KELEG
- Location: Cairo, Egypt
- Timezone: UTC+02
- Github: https://github.com/AMR-KELEG
- Twitter: https://twitter.com/amrkeleg
- Current job: A MSc student and a teacher assistant at Computer and systems department, Faculty of Engineering, Ain Shams university, Cairo, Egypt.
- Experimental blog: https://ak-blog.herokuapp.com
Qualifications
- I graduated as the first of my class of 138 students (Computer and systems department, Faculty of Engineering, Ain Shams University).
- I have successfully participated as a student in GSoC 2016 as part of the GNU Octave organisation.
- I have worked for one year as a full-time machine learning engineer. My role was developing sentiment analysis model for Arabic language.
- As a student, I have participated in online (Google codejam)and on-site (ACM Collegiate programming contest) competitive programming contests.
Throughout those participations, I solved more than 700 problems on different online judges.
- I am interested in open source communities and have made several contributions to open source projects (cltk - gensim - asciinema - octave and apertium).
- I have Completed Udacity's data analysis nanodegree. Throughout those courses, I had to use python to perform analysis on different data-sets.
Skills
- Experience in coding with C++ and python.
- Good command of git and the GitHub process of contribution.
- Usage of Ubuntu as the main OS for more than 3 years.
- Basic knowledge of shell scripting.
Coding challenge
Code repository: https://github.com/AMR-KELEG/apertium-unsupervised-weighting-of-automata
Project Information
Why is it that you are interested in Apertium?
Which of the published tasks are you interested in? What do you plan to do?
Include a proposal, including
* a title, * reasons why Google and Apertium should sponsor it, * a description of how and who it will benefit in society, * and a detailed work plan (including, if possible, a schedule with milestones and deliverables).
Work Plan
| Community Bonding | Communicate with the maintainers and get to know Apertium better.
Solve some issues on Github. |
| Week 1
(27 May - 3 June) |
Implement a baseline model for weigthing automata. |
| Week 2
(4 June - 10 June) |
Develop the first supervised model (Unigram counts).
Write a shell script for generating weights using a tagged corpus. |
| Week 3
(11 June - 17 June) |
Read, Understand and plan for implementing the publication for the first unsupervised model. |
| Week 4
(18 June - 24 June) |
Finalise the first unsupervised model and compare it to the supervised one. |
| Evaluation 1
Deliverables: Two shell scripts for generating weights using both supervised and unsupervised techniques. | |
| Week 5
(29 June - 5 July) |
Read, Understand and plan for implementing the publication for the second unsupervised model. |
| Week 6
(6 July - 12 July) |
Implement the second unsupervised model. |
| Week 7
(13 July - 22 July) |
Read, Understand and plan for implementing the publication for the second unsupervised model. |
| Week 8
(23 July - 12 July) |
Implement the second unsupervised model. |
| Evaluation 2
Deliverables: A shell script for using the second unsupervised model and a plan for implementing the third one. | |
| Week 9
(27 July - 2 August) |
Implement the third unsupervised model. |
| Week 10
(3 August - 9 August) |
Solve issues related to the developed models. |
| Week 11-12
(10 August - 26 August) |
Write the required documentation and merge the code into Apertium's repositories. |
| Final evaluation |
