User:Eiji

Contact Information

   Name:Eiji Miyamoto
   E-mail address:motopon57@gmail.com
   University: University of Manchester
   IRC:thelounge72
   github:https://github.com/yypy22

Why is it that you are interested in Apertium?

   I am intrigued by natural language processing and its usage. Apertium is open-source and free software for machine translation, so apertium match my interest. The community here is welcoming and supportive too. I have taken NLP summer school at the University of Tokyo online and have taken some lectures related to NLP such as Data Science, Machine Learning, and Artificial Intelligence.

Which of the published tasks are you interested in?

   Tokenization for spaceless orthographies in Japanese

What do you plan to do?

   Investigating the suitable tokenizer for east/south Asian languages which usually do not use spaces and implementing it.

Reasons why Google and Apertium should sponsor it

   Apertium translates European languages into other European languages mainly and my proposal for Google Summer of Code 2023 will open up the possibility 
   for future translation in Asian languages which usually do not have space between words in sentences. The Japanese repository in apertium has not been updated for over two years and I believe my task for GSoC2023 will make this repository more active. I also believe my proposal will expand the possibility to create Asian Language and Other language pairs in the future.

Work plan

Time period	Weekly Goal	Details
Week 1 May 29 - June 4	Investigating word segmentation and tokenization from paper, and summarising useful findings into a report	Looking up NLP conference papers on word segmentation or tokenization, and checking popular Japanese tokenizers such as MeCab and Juman.
Week 2: June 5 - June 11	Testing possible algorithms for tokenization and becoming aware of pros and cons of them	N-gram, Maximal matching, Viterbi, SentensePiece, etc...
Week 3: June 12 - June 18	Analyzing drawbacks of current tokenization for apertium-iii and apertium-jpn	Drawing up a report and thinking of what hybrid model needs to cover
Week 4: June 19 - June 25	Producing a hybrid model	Trying to minimize the drawbacks of the current tokenizer and make the model more efficiently
Week 5: June 26 - July 2	Analyzing the model and improving it	Checking efficiency of memory usage and speed of the model. If it has room for improvement, improve it
Week 6: July 3 - July 9	Testing the hybrid model	Testing speed and accuracy with texts
Mid-term Evaluation
Week 7: July 10 - July 16	Converting the model into a faster language	Python model to C++
Week 8: July 17 - July 23	Converting the hybrid model into apertium-jpn	Sending a pull request and refactoring the code
Week 9: July 24 - July 30	Testing and fixing bugs	Testing accuracy and speed with texts and refactoring the code
Week 10: July 31 - August 6	Finalise GSOC project	Writing report and completing tests
Final Evaluation

Other Summer Plans

I do not have any other plan and I can work full time