Paper: Weakly Supervised User Profile Extraction from Twitter

ACL ID P14-1016
Title Weakly Supervised User Profile Extraction from Twitter
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

While user attribute extraction on social media has received considerable attention, existing approaches, mostly supervised, encounter great difficulty in obtaining gold standard data and are therefore limited to predicting unary predicates (e.g., gen- der). In this paper, we present a weakly- supervised approach to user profile extrac- tion from Twitter. Users? profiles from so- cial media websites such as Facebook or Google Plus are used as a distant source of supervision for extraction of their at- tributes from user-generated text. In addi- tion to traditional linguistic features used in distant supervision for information ex- traction, our approach also takes into ac- count network information, a unique op- portunity offered by social media. We test our algorithm on three attribute do...