Combining multiple signals for semanticizing tweets: University of Amsterdam at #Microposts2015
Year: 2015
Type: conference-paper
Abstract: In this paper we present an approach for extracting and linking entities from short and noisy microblog posts. We describe a diverse set of approaches based on the Semanticizer, an open-source entity linking framework developed at the University of Amsterdam, adapted to the task of the #Microposts2015 challenge. We consider alternatives for dealing with ambiguity that can help in the named entity extraction and linking processes. We retrieve entity candidates from multiple sources and process them in a four-step pipeline. Results show that we correctly manage to identify entity mentions (our best run attains an F1 score of 0.809 in terms of the strong mention match metric), but subsequent steps prove to be more challenging for our approach.
Institution: University of Amsterdam
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G6064598850, G7795256738
OpenAlex ID: W2294829975