Media Title Spelling Correction via Frequency Scoring
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Solution Overview
Problem
Existing media asset title correction systems fail to accurately handle creative or intentionally misspelled titles, often misidentifying correct titles due to their reliance on dictionaries, and require significant manpower and time to correct errors in large music catalogs.
Innovation Solution
A media guidance application that receives a media asset, determines its source, generates a reduced list of distinct names, assigns scores based on criteria like views or publication date, and modifies spellings to the most frequent title, allowing for accurate correction even with intentional errors and reducing the workload for content editors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dictionary is used to correct misspelled media asset titles, then spelling errors can be identified, but intentional creative misspellings are incorrectly flagged as errors
Solution Approach 1:
The system uses the media asset data itself (view counts, publication dates, frequency of occurrence) to determine the correct title, rather than relying on external dictionaries. The most frequently occurring title variant is automatically selected as the correct spelling, allowing the data to serve itself for validation purposes.
Solution Approach 2:
The system incorporates feedback from multiple data sources (view counts, publication dates, frequency analysis) to continuously refine title corrections. By analyzing patterns across multiple media assets with similar titles, the system learns which spellings are more likely to be correct based on cumulative evidence from the data.
2Measurement precision
If manual correction of all media asset titles is performed, then accurate corrections can be achieved, but significant time and manpower are required
Solution Approach 1:
The system automatically generates correction recommendations by analyzing the data itself, eliminating the need for manual review of every title. The algorithm self-services by identifying patterns and proposing corrections without human intervention for each individual case.
Solution Approach 2:
Instead of requiring complete manual verification of all titles, the system performs partial automation by generating high-confidence correction recommendations that can be accepted without full manual review. This excessive automation approach handles the majority of cases automatically, with manual review reserved only for edge cases.
3Measurement precision
If all media asset titles are reviewed for corrections, then comprehensive accuracy can be achieved, but the workload becomes unmanageably large
Solution Approach 1:
The system segments the correction task by analyzing titles in groups based on similarity patterns rather than reviewing each title individually. By clustering media assets with similar title structures and analyzing them collectively, the workload is divided into manageable segments that can be processed more efficiently.
Solution Approach 2:
The system applies excessive automation to generate correction recommendations for the entire catalog without manual intervention, then selectively reviews only the cases with lower confidence scores. This approach maintains comprehensive coverage while dramatically reducing the actual manual workload required.
Data Source
AI summary
Systems and methods are disclosed herein for modifying the spelling of a list of names based on a score associated with a first name. The systems and methods may receive a media asset, determine a first source of data corresponding to the media asset, and receive, from the first source of data, a list of names, each name in the list of names being associated with a respective copy of the media asset. The systems and methods may generate a reduced list of names, retrieve a criterion from storage, determine a set of scores corresponding to each name from the reduced list of names based on the criterion, select a first name from the reduced list of names based on the set of scores, and modify the spelling of a second name in the list of names based on the first name.