Paper Info
Title
Fine-grained location extraction from tweets with temporal awareness
Abstract
Twitter is a popular platform for sharing activities, plans, and opinions. Through tweets, users often reveal their location information and short term visiting plans. In this paper, we are interested in extracting fine-grained locations mentioned in tweets with temporal awareness. More specifically, we like to extract each point-of-interest (POI) mention in a tweet and predict whether the user has visited, is currently at, or will soon visit this POI. Our proposed solution, named PETAR, consists of two main components: a POI inventory and a time-aware POI tagger. The POI inventory is built by exploiting the crowd wisdom of Foursquare community. It contains not only the formal names of POIs but also the informal abbreviations. The POI tagger, based on Conditional Random Field (CRF) model, is designed to simultaneously identify the POIs and resolve their associated temporal awareness. In our experiments, we investigated four types of features (i.e., lexical, grammatical, geographical, and BILOU schema features) for time-aware POI extraction. With the four types of features, PETAR achieves promising extraction accuracy and outperforms all baseline methods.
Year
DOI
Venue
2014
10.1145/2600428.2609582
SIGIR
Keywords
Field
DocType
poi,linguistic processing,tweet,temporal awareness,location extraction,crf,twitter
Conditional random field,Data mining,Information retrieval,Computer science,Schema (psychology)
Conference
Citations 
PageRank 
References 
37
0.89
29
Authors
2
Name
Order
Citations
PageRank
Chenliang Li159039.20
Aixin Sun23071156.89