Mongolian Part-of-Speech Tagging with Neural Networks

Autor:	Javkhlan Rentsendorj, Ganchimeg Lkhagvasuren, Oyun-Erdene Namsrai
Rok vydání:	2021
Předmět:	Artificial neural network Computer science Part-of-speech tagging business.industry Treebank Artificial intelligence Part of speech Perceptron computer.software_genre business computer Natural language processing Test data
Zdroj:	Advances in Intelligent Information Hiding and Multimedia Signal Processing ISBN: 9789813367562
DOI:	10.1007/978-981-33-6757-9_15
Popis:	The purpose of this paper is to present a simple neural networks model—multilayer perceptron for Mongolian part-of speech tagging. We used about 1400 manually tagged sentences for training and testing from Mongolian Penn Treebank. The performance of the model is 80.78% which we consider a promising result. Also, another contribution of this work is that we make our testing data online for the sake of the development of Mongolian part-of-speech tagging research.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1c50035f8f364451bb0029e8b0ac8e25 https://doi.org/10.1007/978-981-33-6757-9_15 Zobrazit plný text záznamu