Unsupervised Machine Learning software for Morphology Challenge
📄 Project Abstract
The aim is to develop Unsupervised Statistical Machine Learning software that will be able to segment words of a language into the smallest possible meaningful segment I.e. Morphemes. Morphemes are commonly known as basic vocabulary units that could be used for different tasks such as text understanding, machine translation, statistical language modeling and information retrieval. 1.2 Relevance to degree: This problem relates to the Artificial Intelligence division of my degree. The modules involved include: AI22; Fundamentals of Artificial Intelligence, AI32; Natural Language Processing and SE20; Object Oriented Software Engineering. This problem is taken from the International Morphology Challenge competition that has been running for the last 4 years. 1.3 Morphology Challenge The Morphology Challenge is basically what's been described above but it has variations and follows a methodology. The general outline of the methodology is that participants are given a list of words along wit...
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- ✅ Research Methodology & Data Collection
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- ✅ Findings & Results Discussion
- ✅ Recommendations & Conclusions
- ✅ References & Bibliography
📚 Complete Project Structure
- Problem Statement & Objectives
- Theoretical Framework & Related Studies
- Data Collection & Analysis Methods
- Findings & Statistical Analysis
- Recommendations & Future Research
- Questionnaires, Data, References
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Unsupervised Machine Learning software for Morphology Challenge
Full Citation:
Atif Akhtar. (). Unsupervised Machine Learning software for Morphology Challenge. African and General Studies, 40, 14858.
Citation Formats:
APA
Atif Akhtar. (). Unsupervised Machine Learning software for Morphology Challenge. African and General Studies, 40, 14858.
MLA
Atif Akhtar. "Unsupervised Machine Learning software for Morphology Challenge." African and General Studies, vol. 40, , pp. 14858.
Chicago
Atif Akhtar. "Unsupervised Machine Learning software for Morphology Challenge." African and General Studies 40 (): 14858.
Full Citation:
Atif Akhtar. (). Unsupervised Machine Learning software for Morphology Challenge. African and General Studies, 40, 14858.
Citation Formats:
APA
Atif Akhtar. (). Unsupervised Machine Learning software for Morphology Challenge. African and General Studies, 40, 14858.
MLA
Atif Akhtar. "Unsupervised Machine Learning software for Morphology Challenge." African and General Studies, vol. 40, , pp. 14858.
Chicago
Atif Akhtar. "Unsupervised Machine Learning software for Morphology Challenge." African and General Studies 40 (): 14858.
Document Details
| Author | Atif Akhtar |
|---|---|
| 📁 Field | Computer Science Project Topics |
| 🏷️ Type | Science project topics |
| Pages | 135 Pages |
| Words | 30244 words |
| 📘 Chapters | 1 to 5 Chapters |