Year: 2011
Type: conference-paper
Abstract: We present a novel method for record extrac-tion from social streams such as Twitter. Un-like typical extraction setups, these environ-ments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual mes-sages may not express the full relation to be uncovered, as is often assumed in extraction tasks. We develop a graphical model that ad-dresses these problems by learning a latent set of records and a record-message alignment si-multaneously; the output of our model is a set of canonical records, the values of which are consistent with aligned messages. We demonstrate that our approach is able to accu-rately induce event records from Twitter mes-sages, evaluated against events from a local city guide. Our method achieves significant error reduction over baseline methods.1 1
Authors: Edward Benson, Aria Haghighi, Regina Barzilay
Institution: Massachusetts Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2152336115