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167 lines
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Markdown
167 lines
6.0 KiB
Markdown
---
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title: Manohar Gowdru Shridhara
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published: true
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taxonomy:
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category: [phd2024]
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tag: [lm,nlp,hatespeech]
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author: Daniel Hladek
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---
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# Manohar Gowdru Shridhara
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Beginning of the study: 2021
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## Disertation Thesis
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in 2023/24
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Hate Speech Detection
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Goals:
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- Write a dissertaion thesis
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- Publish 2 A-class journal papers
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## Minimal Thesis
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(preliminary dissertaion and exam in 2022/23)
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Goals:
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- Provide state-of-the-art overview.
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- Formulate dissertation theses (describe scientific contribution of the thesis).
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- Prepare to reach the scientific contribution.
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- Publish 4 conference papers.
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## First year of PhD study
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Goals:
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- Provide state-of-the-art overview.
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- Read and make notes from at least 100 scientific papers or books.
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- Publish at least 2 conference papers.
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- Prepare for minimal thesis.
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Resources:
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- [Hate Speech Project Page](/topics/hatespeech)
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- https://hatespeechdata.com/
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- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
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- [HateBase](https://hatebase.org/)
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- [Resources and benchmark corpora for hate speech detection: a systematic review](https://link.springer.com/article/10.1007/s10579-020-09502-8)
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## Meeting 10.3.22
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- Improvement of the report.
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- Installed Transformers and Anaconda
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Tasks:
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- Try [this model](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) with your own text.
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- Learn how Transformers Neural Network Works. Learn how Roberta Model works Write a short memo about your findings and papers read on this topic.
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- Grab baseline BERT type model and try to finetune it for sentiment classification.
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## Meeting 21.2.22
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- Written a report about HS detection (in progress)
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Tasks:
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- Repair the report (rewrite copied parts, make the paragrapsh be logically ordered, teoreticaly - formaly define the HS detection, analyze te datasets in detail - how do they work. what metric do they use).
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- Install Hugging Face Transformers and come through a tutorial
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## Meeting 31.1.22
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- Read some blogs about transformers
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- Installed and tied transformers
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- Worked on the review paper
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- Picked the Twitter Dataset on keggle
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- still selecting a method
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Open tasks:
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- Continue to work on the paper and share the paper with us.
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- Prepare som ideas for the common discussion about the project.
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- [ ] Try to prepare an experiment with the selected dataset.
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- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
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- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
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- [x] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.
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### Meeting 10.1.22
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- Set up a git account https://github.com/ManoGS with script to prepare "twitter" dataset and "english" dataset for HS detection.
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- confgured laptop with (Anaconda) / PyCharm, pytorch, cuda gone throug some basic python tutorials.
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- Read some blogs how to use kaggle (dataset database).
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- tutorials on huggingface transformers - understanding sentiment analysis.
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Open tasks:
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- [x] Continue to work on the review - with datasets and methods (specified below).
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- [x] Read and make notes about transformers, neural language models and finentuning.
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- [ ] Pick feasible dataset and method to start with.
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- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
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- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
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- [ ] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.
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#### Meeting 16.12.21
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- A report was provided (through Teams).
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- Installed Anaconda and started s Transformers tutorial
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- Started Dive into python book
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Task:
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- Report: Create a detailed list of available datasets for HS.
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- Report: Create a detailed description of the state of the art approaches for HS detection.
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- Practical: Continue with open tasks below. (pick datasetm, perform classification,evaluate the experiment.)
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#### Meeting 10.12.21
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No report (just draft) was provided so far.
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1. Read papers from below and make notes what you have learned fro the papers. For each note make a bibliographic citation. Write down authors of the paper, name paper of the paper, year, publisher and other important information.
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When you find out something, make a reference with a number to that paper.
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You can use a bibliografic manager software. Mendeley, Endnote, Jabref.
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2. From the papers find out answers to the questions below.
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3. Pick a hatespeech dataset.
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4. Pick an approach and Python library for HS classification.
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5. Create a [GIT](https://git.kemt.fei.tuke.sk) repository and share your experiment files. Do not commit data files, just links how to download the files.
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6. Perform and evaluate experiments.
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#### Meeting 10.11.21
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#### First tasks
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Prepare a report where you will explain:
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- what is hate speech detection,
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- where and why you can use hate-speech detection,
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- what are state-of-the-art methods for hate speech detection,
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- how can you evaluate a hate-speech detection system,
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- what datasets for hate-speech detection are available,
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The report should properly cite scientific bibliographical sources.
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Use a bibliography manager software, such as Mendeley.
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Create a [VPN connection](https://uvt.tuke.sk/wps/portal/uv/sluzby/vzdialeny-pristup-vpn) to the university network to have access to the scientific databses. Use scientific indexes to discover literature:
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- [Scopus](https://www.scopus.com/) (available from TUKE VPN)
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- [Scholar](httyps://scholar.google.com)
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Your review can start with:
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- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
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- [HateBase](https://hatebase.org/)
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- [Resources and benchmark corpora for hate speech detection: a systematic review](https://link.springer.com/article/10.1007/s10579-020-09502-8)
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Get to know the Python programming language
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- Read [Dive into Python](https://diveintopython3.net/)
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- Install [Anaconda](https://www.anaconda.com/)
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- Try [HuggingFace Transformers library]( https://huggingface.co/transformers/quicktour.html)
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