Work

Improving efficiency and accuracy in multilingual entity extraction

Year: 2013

Type: conference-paper

Abstract: There has recently been an increased interest in named entity recognition and disambiguation systems at major conferences such as WWW, SIGIR, ACL, KDD, etc. However, most work has focused on algorithms and evaluations, leaving little space for implementation details. In this paper, we discuss some implementation and data processing challenges we encountered while developing a new multilingual version of DBpedia Spotlight that is faster, more accurate and easier to configure. We compare our solution to the previous system, considering time performance, space requirements and accuracy in the context of the Dutch and English languages. Additionally, we report results for 9 additional languages among the largest Wikipedias. Finally, we present challenges and experiences to foment the discussion with other developers interested in recognition and disambiguation of entities in natural language text.

Source: International Conference on Semantic Systems

DOI: 10.1145/2506182.2506198

Authors: Joachim Daiber, Max Jakob, Chris Hokamp, Pablo N. Mendes

Institutions: University of Groningen, University of North Texas, Wright State University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Google, Magyar Tudományos Akadémia Számítástechnikai és Automatizálási Kutatóintézet, Seventh Framework Programme

Award: G2991613951