使用CRF进行Nagamese语的词性标注
Part-of-speech tagging for Nagamese Language using CRF
AI总结：
本文首次针对Nagamese语进行词性标注研究，构建了16,112词元的标注语料库，并应用条件随机场（CRF）机器学习技术，实现了85.70%的标注准确率及85%的F1分数。
摘要：
本文研究了Nagamese语的词性标注，这是自然语言处理（NLP）中的一个重要任务。Nagamese语，又称Naga Pidgin，是一种以阿萨姆语为词汇基础的克里奥尔语，最初主要作为印度东北部Nagas人与阿萨姆人之间贸易交流的沟通工具而发展起来。对于英语、印地语等资源丰富的语言，词性标注方面已进行了大量工作。然而，Nagamese语方面尚未开展任何工作。据我们所知，这是首次对Nagamese语进行词性标注的尝试。本工作旨在识别Nagamese语给定句子的词性。研究创建了一个包含16,112个词元的标注语料库，并应用了名为条件随机场（CRF）的机器学习技术。使用CRF，实现了85.70%的整体标注准确率，精确率和召回率为86%，F1分数为85%。关键词：Nagamese，NLP，词性标注，机器学习，CRF。
英文摘要：
This paper investigates part-of-speech tagging, an important task in Natural Language Processing (NLP) for the Nagamese language. The Nagamese language, a.k.a. Naga Pidgin, is an Assamese-lexified Creole language developed primarily as a means of communication in trade between the Nagas and people from Assam in northeast India. A substantial amount of work in part-of-speech-tagging has been done for resource-rich languages like English, Hindi, etc. However, no work has been done in the Nagamese language. To the best of our knowledge, this is the first attempt at part-of-speech tagging for the Nagamese Language. The aim of this work is to identify the part-of-speech for a given sentence in the Nagamese language. An annotated corpus of 16,112 tokens is created and applied machine learning technique known as Conditional Random Fields (CRF). Using CRF, an overall tagging accuracy of 85.70%; precision, recall of 86%, and f1-score of 85% is achieved. Keywords. Nagamese, NLP, part-of-speech, machine learning, CRF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
