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Proposal Vetting case study for SXSW

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Proposal Vetting

Challenge

SXSW receives thousands of conference proposals every summer, each of which must be reviewed and graded by internal staff.

Solution

KUNGFU.AI designed and trained a deep learning NLP model to automatically review and predict the grade of conference proposals. The model examines multiple features for each proposal including, title, description, track name, target audience description, speaker names, emails, and Twitter follower counts.

Outcome

Streamlined the review process by rank ordering proposals based on the model’s predictions — saving time and ensuring the highest quality content is scheduled first.

NLP
SXSW

Proposal Vetting

Challenge

SXSW receives thousands of conference proposals every summer, each of which must be reviewed and graded by internal staff.

Solution

KUNGFU.AI designed and trained a deep learning NLP model to automatically review and predict the grade of conference proposals. The model examines multiple features for each proposal including, title, description, track name, target audience description, speaker names, emails, and Twitter follower counts.

Outcome

Streamlined the review process by rank ordering proposals based on the model’s predictions — saving time and ensuring the highest quality content is scheduled first.

NLP
SXSW

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