User:AMR-KELEG/GSoC19 Proposal

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

• LinkedIn: https://www.linkedin.com/in/amr-mohamed-hosny/

• 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