##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Using NLP to generate Quizzes
- Quizzes/Questions are required by students for self-evaluation and by teachers to test students.
- However, quiz creation currently requires manual effort and understanding of the topic which making the process difficult to adapt and scale.
- Multiple-Choice Questions have been proved to evaluate student’s understanding of concepts and can exhibit broad ranges of difficulty and therefore, test different levels of understanding.
- However, MCQs require challenging options which can be cumbersome to pick.
- Using NLP to generate Multiple-Choice Questions from plain-text
- Statistically determines best entities to blank
- Generate semantically similar options based on context and corpus
- Better than subjective questions MC questions tests
- A user-interface to generate quizzes
- From any document
- On any topic in any genre (Educational or Recreational)
- From any paragraph
- Problem Statement
- How it’s done
- Brief introduction to NLP and NER (Named Entity Recognition)
- Using Named Entity Recognition to pick questions
- Ranking potential questions
- Picking relevant/similar options to accompany answer in MCQ
- Deploying the solution
- Scope and application of the solution
- Machine Learning Engineer at Freshworks since 2019. Work on building AI models for Freshsales, our Sales CRM.
- Worked with a number of startups on NLP and ML problems.
- Google Summer of Code’18 under Debian