Name: Tinuade Adeleke
IRC nickname: Unit-Ade
Web Profile: about.me/tinuade
Typical working hours (9:00am - 5:00pm GMT+1)
Wikimedia tools such as Citation Hunt use Citation Needed tags to provide microtasks to users interested in improving Wikipedia’s reliability, highlighting these unsourced statements and asking users to find a relevant citation. Releasing public, updated data about which sentences need citations in Wikipedia can be incredibly useful to augment the potential of such tools, as well as foster the creation of new community tools that leverage micro-tasks to improve Wikipedia’s reliability.This data can be further enriched by discovering more sentences missing citations using the machine learning model developed by the Research team.
The “Citation Needed” model takes as input a sentence from a Wikipedia article and its section title, and gives as output a “citation needed” score reflecting whether the sentence should have a citation or not
We want to scale this process up by designing an end-to-end model that automatically parses the contents of Wikipedia articles to extract unsourced sentences and their section titles, classify these sentences using the machine learning model to detect which of those are actually missing citations, and release the output in form of periodic data dumps.
The Media Wiki Api is called periodically by a cronjob to fetch the articles from Wikipedia data dumps, a parser then extract sentences in the article along side the section title. It also checks for unsourced sentences. The result of this stage is then passed to the Citation Needed model for classification Each time it runs, the results of the sentences that need citation are then saved as a data dump. As a stretch goal we can also have API's that allows this data to accessed
Dec 3 - Dec 7 : Community bonding period. Communicating with mentors on refining your project proposal, finalizing deadlines and setting milestones. Studying existing tools.Planning the design of the tool. Adding and structuring the corresponding tasks in
Dec 8 - Dec 15: Work with Media Wiki API to access articles and get a clear understanding of the API
Dec 15 - Dec 22: Start work on the article parser. Be able to extract out section titles from articles
Dec 23 - Dec 29: Continue work on the parser, identify unsourced sentences in sections
Dec 30 - Dec 5: Integrate parser with the Citation Needed model for testing
Jan 5 - Jan 12: Work on storing the result from citation needed model as data dumps
Jan 13 - Jan 26: Develop Unit tests
Jan 27 - Feb 10: Evaluate performance and make modification to the system. Improvements based on the feedback responses received and find and document useful features that can be added
Feb 11 - Mar 3: Bug fixes, Writing documentation and Updating appropriate guides. Code cleanup for submission.
Work on a separate branch on git and uploading code to the forked repository almost on a daily basis. Creating pull requests as and when a complete feature is done.
Online on IRC in my working hours ( 9am to 5pm GMT+1) to collaborate with my mentors
Communication on tasks will be through commenting on subtasks to the project created on Phabricator.
Weekly reports will be published in my meta wiki user page
Publishing on my blog the summary of a task at the end of a task period as above in the timeline
Keeping an open mind to learn and achieve the best results
I also hope to create a community for wikimedia foundation here in my sphere of influence. Where people can learn and contribute to open source.
I'm A fresh Graduate of the Obafemi Awolowo University Ile-Ife. Heard about this program from a friend. During the duration of the program Outreachy would be my first priority since I won't have any other commitments.
Contributing to open source can be a rewarding way to learn, teach, and build experience in just about any skill you can imagine. I want that one opportunity that gives me several other opportunities to Improve on existing skills, Interact with a greater perspective, meet and work with people interested in similar things, learn people skills, build public artifacts and grow a reputation amongst many others.
More Interestingly, I would be contributing in my own little way something that would make the world's largest free encyclopedia, richer in content.
I have had experience with python, chat bots, API's and databases(SQL and NoSQL) while working on the following projects
Facebook Tour Chatbot https://github.com/tinumide/Facebook-bot
A chatbot that helps find tourist centres in your location
Facebook Quotebot https://github.com/tinumide/Quote_bot
A chatbot that helps find quotes based on a particular subject e.g Quotes on love
Invent Edu is a skill-sharing web application where learners meet with tutors. I wrote the Back end of this web application using Flask
A python package that creates a word cloud from your twitter timeline
I interned at inventone where I worked for improving the features of a proprietary software as well.
I love to participate in hackathons and have participated in a few over the years where I demonstrated critical thinking
IEEE Extreme 2019
Microsoft Leap Hackathon 2019
NIBSS Hackathon 2018
Girls with Grits 2017
During the contribution period with wikimedia on the project topic "A system for releasing data dumps from a classifier detecting unsourced sentences in Wikipedia" Three tasks were given to be completed during
Open None T233709 Onboarding Task: I got familiar with the machine learning models for Citation Need
I read the documentation about the Research Project, and become familiar with the codebase for the machine learning models , as well as with basic notions and functions of the Keras library for Python
Open None T234519 Your first task:
I classified sample statements using Citation Needed Models
Open None T234606 Your second task:
In this task, I exercised simple parsing of a Wikipedia article and classifying some of its sentences.
I wrote a script in python that
1- Receives as input the title of a English Wikipedia article.
2- Retrieves the text of that article from the MediaWiki API. If using Python, consider using python-mwapi for this.
3- Identifies individual sentences within that text, along with the corresponding section titles. If using Python, mwparserfromhell can help you work with wiki markup.
4- Runs those sentences through the model to classify them.
5- Outputs the sentences, one per line, sorted by score given by the model.
Any Other Info
Add any other relevant information such as UI mockups, references to related projects, a link to your proof of concept code, etc