The significance of computers, handheld devices and educational applications are not deniable in the modern world of today. The interest of applying AI and Machine Learning algorithms in this technologies is growing. Texts are entered to these devices using word processing programs as well as other techniques. Word prediction is one of the techniques and assistive technology for people with disabilities. The main purpose of word prediction is to facilitate data entry and help dyslexic people in reducing writing errors. Dyslexia is a learning problem some kids and few adults have. Dyslexia makes it tough to read and spell. For developing countries such as Ethiopia this kind of problems are neglected and the language spoken within the country are under resourced. Therefore this work has a major contribution to those who have this type of disability in their own language. Prediction can also help education by facilitating data entry. Building prediction model with better speed, grammatically correct suggestions and less search space is the main focus of the research. Amharic is used by a large number of populations, however no significant work is done on the topic of word sequence prediction. In this study, Amharic word sequence prediction model is developed with statistical methods using Hidden Markov Model by incorporating detailed parts of speech tag, some morphological features and user profiling or adaptation. Evaluation of the model is performed using developed prototype and keystroke savings (KSS) as a metrics. According to our experiment, prediction results using a bi-gram with morphological features and detailed Parts of Speech tag model has higher KSS and performed better compared those without Parts of Speech tag. Therefore, statistical approach with detailed POS, morphological features like gender, number, and person with suggested root or stem words using voice, tense, aspect, affixes statistical information and grammatical agreement rules of the language has quite good potential on word sequence Prediction for Amharic language.
I would not like to sound offensive, but this paper is clearly below the standards of the conference, and outside any academic orthodoxy for the matter:
- It is only 3 pages long including references, and does not even follow the conference template.
- It has only 3 sections ("introduction", "methodology" and "conclusions") and 2 subsections ("background of study" and "limitation"), and all of them are only one paragraph long.
- The work done is not adequately described, so it is not possible to say much about it, but it seems clear that there is no novelty nor sufficient rigor in it. The problem tackled is defined as "word prediction", which seems to be some form of language modeling. The proposed method combines HMMs with n-grams and morphological and POS features, which are all well established and should be considered more of a (nowadays outdated) baseline. There is no proper evaluation: the experimental settings are not described, and only one number is reported, with nothing to compare to.
To the authors: Please do not feel discouraged by my review. Your motivation (helping people with dyslexia in Ethiopia) is certainly laudable, but neither the work carried out nor its presentation meets the standards of our research community. I would suggest that you check some of the accepted papers in last year's conference to get a sense of what is expected. I assume that you are new to the field. Don't feel the need to publish your own research from the first day, and take your time to become familiar with the field and study the basic concepts. It takes time, but we all had to go through it :)