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| README.md | ||
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| Manohar Gowdru Shridhara | true |
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Manohar Gowdru Shridhara
Beginning of the study: 2021
repository: https://git.kemt.fei.tuke.sk/mg240ia
Disertation Thesis
in 2023/24
Hate Speech Detection
Goals:
- Write a dissertaion thesis
- Publish 2 A-class journal papers
Minimal Thesis
(preliminary dissertaion and exam in 2022/23)
Goals:
- Provide state-of-the-art overview.
- Formulate dissertation theses (describe scientific contribution of the thesis).
- Prepare to reach the scientific contribution.
- Publish 4 conference papers.
First year of PhD study
Goals:
- Provide state-of-the-art overview.
- Read and make notes from at least 100 scientific papers or books.
- Publish at least 2 conference papers.
- Prepare for minimal thesis.
Resources:
- Hate Speech Project Page
- https://hatespeechdata.com/
- Hate speech detection: Challenges and solutions
- HateBase
- [Resources and benchmark corpora for hate speech detection: a systematic review] (https://link.springer.com/article/10.1007/s10579-020-09502-8)
14.7:
- Pick a feasible dataset and method to start with. - Done
- Try to prepare an experiment with the selected dataset. - Done
- Summarize the results in the table and publish the table on git. . - Done
- Finish experiments, upload source codes into git, and describe the experiments. . - Done
- For preparing a web application with demo, learn about streamlit. . - Done
Open tasks:
- Focus on making a baseline experiment for sentiment classification using classical methods, such as Transformers.
Read Papers :
- https://aclanthology.org/2020.peoples-1.6.pdf
- https://aclanthology.org/2022.ltedi-1.14/
- https://arxiv.org/abs/2108.03867
- https://arxiv.org/pdf/2112.15417v4.pdf
- https://arxiv.org/ftp/arxiv/papers/2202/2202.04725.pdf
- https://github.com/manikandan-ravikiran/DOSA/blob/main/EACL_Final_Paper.pdf
- https://aclanthology.org/2020.icon-main.13.pdf
- http://ceur-ws.org/Vol-3159/T6-4.pdf
- https://www.researchgate.net/publication/353819476_Hope_Speech_detection_in_under-resourced_Kannada_language
- https://www.researchgate.net/publication/346964457_Creation_of_Corpus_and_analysis_in_Code-Mixed_Kannada-English_Twitter_data_for_Emotion_Prediction
- https://www.semanticscholar.org/paper/Detecting-stance-in-kannada-social-media-code-mixed-SrinidhiSkanda-Kumar/f651d67211809f2036ac81c27e55d02bd061ed64
- https://www.academia.edu/81920734/Findings_of_the_Sentiment_Analysis_of_Dravidian_Languages_in_Code_Mixed_Text
- https://competitions.codalab.org/competitions/30642#learn_the_details
- https://paperswithcode.com/paper/creation-of-corpus-and-analysis-in-code-mixed
- https://paperswithcode.com/paper/hope-speech-detection-in-under-resourced#code
Meeting 13.6.
- Implemented a Mayfly and Horse Heard Algorithms in Python and Matlab for HS datasets.
- Written a draft of a paper.
- Performed experiments on HS with Word2Vec, FastText, OneHot.
Tasks:
- Implement open tasks from the previous meetings !!!!!!!!
- Share Scripts with GIT and Drafts with Online Word or Docs !!!
- try https://huggingface.co/cardiffnlp/twitter-roberta-base-hate, try to repeat the training and evaluation
Meeting 24.5.
- shared colab notebook, with on-going implementation of mayfly algorithm for preprocessing in sentiment recognition in a twitter dataset.
Tasks:
- Implement open tasks from the previous meetings !!!
- Focus on making a baseline experiment for sentiment classification using classcal methods, such as Transformers.
- Consider using pre-trained embeddings. FastText, word2vec, sentence-transformers, Labse, Laser,
Supplemental tasks:
- Fininsh the mayfly implementation
Meeting 20.5.
- learned about Firefly / mayfly optimization algorithm.
- read ten papers,
- wrote 1 page abstract about possible system, based od DBN.
Meeting 25.4.
- Learned aboud deep learning lifecycle / evaluation, BERT, RoBERTa, GPT
- Tried HF transformers, Spacy, NLTK, word embeddings, sentence transformers.
- Set up a repo with notes: https://git.kemt.fei.tuke.sk/mg240ia
Tasks:
- Publish experiments into the repository.
- Prepare a paper for publication in faculty proceedings http://eei.fei.tuke.sk/#!/
- Send me draft in advance.
Suplemental tasks:
- For presentation of the results, learn about https://wandb.ai/. This can dispplay results (learning curve, etc.)
- For preparing a web aplication with demo, learn about streamlit.
Meeting 12.4.
- Created repositories, empty so far.
- 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".
- The experiments are based on BERT (which kind?), Tweet Emotion Intensity.
- Prepared colab notebook with experiments.
Tasks:
- Finish experiments, upload source codes into git, provide a description of the experiments.
- 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.
- Learn about "Sentence Transformers".
- Summarize the results in the table, publish the table on git.
- [-] Use Markdown for formatting. There is "Typora".
- [-] Continue to improve the SCYR paper.
- If you have some conference in mind, tell me.
Meeting 25.3.22
- Learned about Transformers, BERT, LSTM and RNN.
- Tried HuggingFace transformers library
- Started Google Colab - executing sentiment analysis, hf transformers pipeline functions.
- prepared datasets: twitter-roberta Datasets. Experiments a re riunnig, no results yet.
- prepared a short note about nlp and neural networks.
- still working on the SCYR paper
Tasks:
- [-] finish experiments about sentiment and present results.
- [-] create a repository on git.kemt.fei.tuke.sk and upload your experiments, results and notes. Use you student creadentials.
- [-] continue working on "SCYR" review paper, consider publishing it elswhere (the firs version got rejected).
- [-] prepare an outline for another paper with sentiment classification.
Meeting 10.3.22
- Improvement of the report.
- Installed Transformers and Anaconda
Tasks:
- Try this model with your own text.
- 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.
- Pick a dataset:
- https://huggingface.co/datasets/sentiment140 (english)
- https://www.clarin.si/repository/xmlui/handle/11356/1054 (multilingua)
- https://huggingface.co/datasets/tamilmixsentiment (english tamil code switch)
- Grab baseline BERT type model and try to finetune it for sentiment classification.
- For finetuning and evaluation you can use this scrip https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification
- For finetuning you will need to install CUDA and Pytorch. It can work on CPU or NOT.
- If you need GPU, use the school server idoc.fei.tuke.sk or google Colab.
- Continue working on the paper.
- Remind me about the SCYR conference payment.
Meeting 21.2.22
- Written a report about HS detection (in progress)
Tasks:
- 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).
- Install Hugging Face Transformers and come through a tutorial
Meeting 31.1.22
- Read some blogs about transformers
- Installed and tied transformers
- Worked on the review paper
- Picked the Twitter Dataset on keggle
- still selecting a method
Open tasks:
- Continue to work on the paper and share the paper with us.
- Prepare som ideas for the common discussion about the project.
- Try to prepare an experiment with the selected dataset.
- You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- 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/.
Meeting 10.1.22
- Set up a git account https://github.com/ManoGS with script to prepare "twitter" dataset and "english" dataset for HS detection.
- confgured laptop with (Anaconda) / PyCharm, pytorch, cuda gone throug some basic python tutorials.
- Read some blogs how to use kaggle (dataset database).
- tutorials on huggingface transformers - understanding sentiment analysis.
Open tasks:
- Continue to work on the review - with datasets and methods (specified below).
- Read and make notes about transformers, neural language models and finentuning.
- Pick feasible dataset and method to start with.
- You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- 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/.
Meeting 16.12.21
- A report was provided (through Teams).
- Installed Anaconda and started s Transformers tutorial
- Started Dive into python book
Task:
- Report: Create a detailed list of available datasets for HS.
- Report: Create a detailed description of the state of the art approaches for HS detection.
- Practical: Continue with open tasks below. (pick datasetm, perform classification,evaluate the experiment.)
Meeting 10.12.21
No report (just draft) was provided so far.
- 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. When you find out something, make a reference with a number to that paper. You can use a bibliografic manager software. Mendeley, Endnote, Jabref.
- From the papers find out answers to the questions below.
- Pick a hatespeech dataset.
- Pick an approach and Python library for HS classification.
- Create a GIT repository and share your experiment files. Do not commit data files, just links how to download the files.
- Perform and evaluate experiments.
Meeting 10.11.21
First tasks
Prepare a report where you will explain:
- what is hate speech detection,
- where and why you can use hate-speech detection,
- what are state-of-the-art methods for hate speech detection,
- how can you evaluate a hate-speech detection system,
- what datasets for hate-speech detection are available,
The report should properly cite scientific bibliographical sources. Use a bibliography manager software, such as Mendeley.
Create a VPN connection to the university network to have access to the scientific databses. Use scientific indexes to discover literature:
Your review can start with:
- Hate speech detection: Challenges and solutions
- HateBase
- Resources and benchmark corpora for hate speech detection: a systematic review
Get to know the Python programming language
- Read Dive into Python
- Install Anaconda
- Try HuggingFace Transformers library