However, you may either borrow or build the underlying ai technology components.

Published by Jeannie R. Ferrell

Dec 8, 2022

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Topic: research project on anything related to metaverse , AR/VR and anything from the list below related to NLP
15 pages. 15 Q1 and Q2 resources.
In this Natural Language Processing (NLP) project, you will have to work on a project of your choice in natural language processing. Once you select the problem you want to address, you will have to:
review the literature relevant to the problem
design and implement an algorithm that solves the problem
evaluate the algorithm on a gold-standard data set
4. write a report describing the problem and your findings
2. Suggested Topics
In this course, we will explore topics related to Natural Language Processing. Below you will find the list of topics. Your topic Topics are not limited to the suggested list you can choose any NLP topic and follow the guidelines in this assignment.
· – Intelligent Language Tutoring Systems
· – Complex network analysis of text corpora
· – Ontologies and linguistic resources (corpora, electronic dictionaries,
treebanks, etc.)
· – Transliteration, transcription and diacritization
· – Part of speech tagging
· – Morphological analysis and generation
· – Shallow, deep, and partial parsing
· – Machine translation
· – Word sense and syntactic disambiguation
· – Semantic analysis
· – Information retrieval
· – Information extraction
· – Question answering
· – Text clustering and classification
· – Text summarization
· – Text and web content mining
· – Named entity recognition
2
– Complex network analysis of text corpora
Once you have chosen a particular topic, you will need more background on it and will end up reading and citing many papers in the field.
3. Project Types
Projects may be of several types:
1. Rational reconstruction: Based on material from the literature, re-build an NLP program and replicate its results. This reconstruction need not be completely faithful to the original program, but instead it should include improvements or guesswork where the literature may not completely describe the system’s construction.
2. Experimental study: For this project, the emphasis is not on programming, but rather on testing known technology on novel data sets, or against other known technologies. This is the whole raison d’etre for the machine learning repository. For this project, you may either re-build some AI/NLP system, or use software that is available off-the-shelf (ideally, in the public domain). Of course, the less programming you do, the more work I will expect in data analysis and analysis of your experimental results.
3. Application project: In this type of project, the emphasis will be on the application of AI technology to some industry or domain. I will expect programming in these cases, since each domain typically has specific requirements. However, you may either borrow or build the underlying AI technology components.
You will have to write a project report, following the ACL format http://www.ling.ohio-state.edu/acl08/stylefiles.html. Pls. indicate which project type you selected. The report has to be in word or PDF format. It should include the following sections:
Executive summary. A summary that describes the contents of the report.
Introduction. An introduction to the problem, with a brief description of the problem you are addressing, motivation (why the problem is important, what are possible applications), examples that illustrate the problem, research
questions.
Related work. An overview of previous related work on the problem you are
addressing. You should aim to review at least 10 related papers, ideally more.
You have to link the research questions to the literature review.
Method A detailed description of the proposed algorithm (or research methodology), starting with the main idea, an explanation of why you think this should work, examples of how the algorithms work, etc. Anything that you think would be relevant to explain the algorithm should be included in this section. Ideally, somebody else should be able to replicate your algorithm just by reading this section. Do not include code; at most, you can include some pseudo-code of your algorithm, if you feel that would make the algorithm description more clear. The section should be a proposal to answer the
research questions.
Evaluation An evaluation of your algorithm on a gold-standard data set.
Describe the data set, how it was created (or include a reference to previous work, if the data set was created by other people), provide some statistics relevant to the characteristics of the data set.
Discussion of results A discussion of the evaluation, including an analysis of the errors that are not solved, examples of what worked and what did not, possible explanations for observed errors. Formulated answers of the research questions.
Conclusions and future work The conclusions you draw from your experiments, how you answered the research questions, possible comparison with related research, and improvements/recommendations you could make in future work.
ACL Bibliography & Citations. Properly cite papers you included in the reference section. You have to make acceptable coverage from the literature.

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