Year: 2020
Type: conference-paper
Abstract: This paper adds to the available resources for the under-resourced language Urdu by converting different types of existing treebanks for Urdu into a common format that is based on Universal Dependencies. We present comparative results for training two dependency parsers, the MaltParser and a transition-based BiLSTM parser on this new resource. The BiLSTM parser incorporates word embeddings which improve the parsing results significantly. The BiLSTM parser outperforms the MaltParser with a UAS of 89.6 and an LAS of 84.2 with respect to our standardized treebank resource.
Source: KOPS (University of Konstanz)
Authors: Toqeer Ehsan, Miriam Butt
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3029962201