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98 lines
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Markdown
98 lines
3.3 KiB
Markdown
---
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title: Manohar Gowdru Shridharu
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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 Shridharu
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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 10.12.21
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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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