Work

Improving NER in Arabic Using a Morphological Tagger

Year: 2008

Type: conference-paper

Abstract: We discuss a named entity recognition system for Arabic, and show how we incorporated the information provided by MADA, a full morphological tagger which uses a morphological analyzer.Surprisingly, the relevant features used are the capitalization of the English gloss chosen by the tagger, and the fact that an analysis is returned (that a word is not OOV to the morphological analyzer).The use of the tagger also improves over a third system which just uses a morphological analyzer, yielding a 14% reduction in error over the baseline.We conduct a thorough error analysis to identify sources of success and failure among the variations, and show that by combining the systems in simple ways we can significantly influence the precision-recall trade-off.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/47zx42oawm7v

Authors: Benjamin Farber, Dayne Freitag, Nizar Habash, Owen Rambow

Institutions: Fair Isaac Corporation (United States), Columbia University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Defense Advanced Research Projects Agency

Awards: G2515541506, G3190074090