https://github.com/dcavar/antisemitismdatathon2020
This is project material for the Antisemitism Datathon and Hackathon 2020 at Indiana University
Science Score: 13.0%
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○Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary
Keywords
Repository
This is project material for the Antisemitism Datathon and Hackathon 2020 at Indiana University
Basic Info
- Host: GitHub
- Owner: dcavar
- License: apache-2.0
- Default Branch: master
- Size: 1.21 MB
Statistics
- Stars: 6
- Watchers: 3
- Forks: 1
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Antisemitism Datathon 2020
(C) 2020 by Damir Cavar and Günther Jikeli
The information and code examples are licensed under the Apache License Version 2.0.
This is project material for the Antisemitism Datathon and Hackathon 2020 at Indiana University at Bloomington.
This Datathon and Hackathon is a collaborative project of Günther Jikeli from the Institute for the Study of Contemporary Antisemitism and Damir Cavar's NLP-Lab.org at Indiana University at Bloomington!
Relevant Links
Technologies
We provide an NLP pipeline with detailed linguistic analysis: tokenization, lemmatization, splitting text into sentences, part-of-speech tagging, named entity annotation, dependency parsing, constituent parsing, sentiment detection, and coreference and anaphora resolution:
- NLP Pipeline as RESTful API (provided through the courtesy of Semiring Inc.)
This pipeline is an integration of RESTful Microservices that take as input some text and return a JSON-NLP formated output. This service requires a login and password. We will share this with you during the meetings.
The linguistic annotations enable modeling of classifiers using deeper linguistic analysis.
In addition to that, we provide code examples for the following NLP and Machine Learning libraries, to develop probabilistic, neural, and/or symbolic classifiers for the corpus material:
Data Sets and Formats
The Antisemitism Twitter corpus will be provided to you in a specific CSV format. We will also provide a CoNLL formated version of the data. These are formats that the different Machine Learning libraries for NLP mentioned above can read.
You might want to have a look at the different corpus or linguistic data formats:
Tools
For testing the NLP API RESTful Microservices you might want to have a look at tools like:
Owner
- Name: Damir Cavar
- Login: dcavar
- Kind: user
- Location: Bloomington, IN
- Company: Indiana University
- Website: http://damir.cavar.me/
- Repositories: 29
- Profile: https://github.com/dcavar
GitHub Events
Total
Last Year
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Damir Cavar | d****r@m****m | 8 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0