A Cross-Task Flexible Transition Model for Arabic Tokenization, Affix Detection, Affix Labeling, POS Tagging, and Dependency Parsing
Year: 2013
Type: conference-paper
Abstract: This paper describes cross-task flexible transition models (CTF-TMs) and demonstrates their effectiveness for Arabic natural language processing (NLP).NLP pipelines often suffer from error propagation, as errors committed in lower-level tasks cascade through the remainder of the processing pipeline.By allowing a flexible order of operations across and within multiple NLP tasks, a CTF-TM can mitigate both cross-task and within-task error propagation.Our Arabic CTF-TM models tokenization, affix detection, affix labeling, partof-speech tagging, and dependency parsing, achieving state-of-the-art results.We present the details of our general framework, our Arabic CTF-TM, and the setup and results of our experiments.
DOI: 10.18653/v1/w13-4904
Author: Stephen Tratz
Institutions: DEVCOM Army Research Laboratory, Adelphi Laboratory Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2251872153