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211 lines
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Markdown
211 lines
8.3 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 12.4.
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- Created repositories, empty so far.
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- Tried to replicate the results from "Emotion and sentiment analysis of tweets using BERT" paper and "Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data".
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- The experiments are based on BERT (which kind?), Tweet
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Emotion Intensity.
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- Prepared colab notebook with experiments.
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Tasks:
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- Finish experiments, upload source codes into git, provide a description of the experiments.
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- Try to improve the results - try different kind of BERT - roberta, electra, xl-net. Can "generative models" be used? (gpt, bart, t5). Can "sentence transformers be used" - labse, laser.
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- Learn about "Sentence Transformers".
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- Summarize the results in the table, publish the table on git.
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- Use Markdown for formatting. There is "Typora".
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- Continue to improve the SCYR paper.
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- If you have some conference in mind, tell me.
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## Meeting 25.3.22
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- Learned about Transformers, BERT, LSTM and RNN.
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- Tried HuggingFace transformers library
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- Started Google Colab - executing sentiment analysis, hf transformers pipeline functions.
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- prepared datasets: twitter-roberta Datasets. Experiments a re riunnig, no results yet.
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- prepared a short note about nlp and neural networks.
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- still working on the SCYR paper
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Tasks:
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- [-] finish experiments about sentiment and present results.
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- [-] create a repository on git.kemt.fei.tuke.sk and upload your experiments, results and notes. Use you student creadentials.
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- [-] continue working on "SCYR" review paper, consider publishing it elswhere (the firs version got rejected).
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- [-] prepare an outline for another paper with sentiment classification.
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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 training works. Learn how BERT model finetuning works. Write a short memo about your findings and papers read on this topic.
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- Pick a dataset:
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- https://huggingface.co/datasets/sentiment140 (english)
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- https://www.clarin.si/repository/xmlui/handle/11356/1054 (multilingua)
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- https://huggingface.co/datasets/tamilmixsentiment (english tamil code switch)
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- Grab baseline BERT type model and try to finetune it for sentiment classification.
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- For finetuning and evaluation you can use this scrip https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification
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- For finetuning you will need to install CUDA and Pytorch. It can work on CPU or NOT.
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- If you need GPU, use the school server idoc.fei.tuke.sk or google Colab.
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- Continue working on the paper.
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- Remind me about the SCYR conference payment.
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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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