泰卢固语命名实体识别的比较研究

Proceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2017-12-08 DOI:10.1145/3158354.3158358

SaiKiranmai Gorla, N. B. Murthy, Aruna Malapati

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引用次数: 2

摘要

在本文中，我们将三种分类学习算法应用于泰卢固语命名实体识别(NER)任务，并在泰卢固语数据集(南亚和东南亚语言NER (NERSSEAL)竞赛)上对这三种学习算法进行了比较研究。实证结果表明，支持向量机在该数据集上达到了54.78%的最佳f度量值。

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A Comparative Study of Named Entity Recognition for Telugu

In this paper, we apply three classification learning algorithms to Telugu Named Entity Recognition (NER) task and we present a comparative study between these three learning algorithms on Telugu dataset (NER for South and South-East Asian Languages (NERSSEAL) Competition). The empirical results show that Support Vector Machine achieves the best F-measure of 54.78% on the dataset.

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Proceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation

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