AUTOMATED TV METADATA EXTRACTION
TV Control
General info
Client
The varied requirements of different media channels and broadcasters make it challenging to prepare program descriptions.
There are ways to automate the task, but even these require lots of manual effort owing to the preparation of templates and missing metadata.
Project
DLabs.AI built a solution to kill two birds with one stone. First, we looked to extract metadata and possible templates from existing descriptions.
Then, we tackled the creation of the descriptions themselves.
About the project
Problem
It’s tricky to find metadata such as director, actors, year, or genre in a program description. It’s even more problematic to then accurately port that information to a new database.
Solution
We built a hybrid extraction method based on regular expressions, linguistic rules, and statistical language models, using dependency analysis to decompose templates.
Results
We achieved a 1-2% error rate in extracted metadata (depending on the field), with 2.5 metadata items extracted on average per description.
Project duration
5 months
Technologies used
The path to success
Step 1: Clean the metadata
Improve the metadata for training, finding external sources to fill and validate existing metadata extraction
Step 2: Create a pipeline for metadata extraction
Develop and train algorithms to extract specific information from descriptions
Step 3: Template analysis
Turn the description into an abstract sentence structure to allow us to reuse it within the template itself
See it in action
CLIENTS OPINION
They have great attention to detail and background
knowledge in the subject.
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