Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task
This work addresses a specific information extraction task for knowledge base construction, but it is incremental as it builds on existing methods for a competition.
The paper tackled the problem of scoring the relevance of triples for professions and nationalities using distant supervision, achieving first place in Kendall's Tau out of 21 teams in a competition.
We describe the system that our FMI@SU student's team built for participating in the Triple Scoring task at the WSDM Cup 2017. Given a triple from a "type-like" relation, profession or nationality, the goal is to produce a score, on a scale from 0 to 7, that measures the relevance of the statement expressed by the triple: e.g., how well does the profession of an Actor fit for Quentin Tarantino? We propose a distant supervision approach using information crawled from Wikipedia, DeletionPedia, and DBpedia, together with task-specific word embeddings, TF-IDF weights, and role occurrence order, which we combine in a linear regression model. The official evaluation ranked our submission 1st on Kendall's Tau, 7th on Average score difference, and 9th on Accuracy, out of 21 participating teams.