尼泊尔语回指消解的机器学习方法

Apurbalal Senapati, Arun Poudyal, P. Adhikary, Sahana Kaushar, Anmol Mahajan, Baidya Nath Saha
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引用次数: 2

摘要

在本文中,我们尝试了一种机器学习(ML)方法来实现尼泊尔语的回指解析(AR)系统。尼泊尔语是一种资源有限的语言,它是利用机器学习来解决回指的先驱方法之一。为了这项工作,我们开发了我们自己的标准格式的数据集。数据已经标注了必要的信息,如词性(POS)、命名实体、分块信息、性别、数字、人等。我们将用于训练和测试目的的数据以大约5:1的比例划分,并将ML分类器用于训练和测试。结果显示出令人鼓舞的进一步进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Machine Learning Approach to Anaphora Resolution in Nepali Language
In this paper, we attempt a machine learning (ML) approach to Anaphora Resolution (AR) system in Nepali language. It is one of the pioneering approaches in anaphora resolution using machine learning in Nepali language, which is a resource-limited language. For this work, we have developed our own data set in the standard format available in this domain. Data has been tagged with the necessary information like Parts-of-speech (POS), Named entity, Chunking information, Gender, Number, Person, etc. We divided the data for training and testing purposes in approximately 5:1 ratio and ML classifiers are used for training and testing. Results show encouraging for further progress.
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