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首页> 外文期刊>Journal of Physics: Conference Series >Fine-Grained Named Entity Recognition in Question Answering with DBpedia
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Fine-Grained Named Entity Recognition in Question Answering with DBpedia

机译：DBpedia问答中的细粒度命名实体识别

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摘要

Named Entity Recognition in Question Answer tasks can help the language understanding. Traditional NER task usually solved by a supervised model, which need a large number of annotated corpora and extract context information as feature for training, however questions are usually short sentences, with little context information, little public annotated corpora. On the other hand coarse-grained named entities' types cannot offer enough information for question understanding. In this paper, we propose a novel model to address both problems, using a distant supervised method. Firstly, we use the web search to obtain more relevant information. Secondly, we present a greedy n-grams algorithm to extract the entity mentions. Finally, we use the kNN to classification and get fine-gained entity types combining with the entity mentions which can link with DBpedia. Experimental results show that our model outperforms various state-of-art systems in public dataset--TREC.
机译：问题解答任务中的命名实体识别可以帮助理解语言。传统的NER任务通常由监督模型解决，该模型需要大量带注释的语料库并提取上下文信息作为训练的特征，但是问题通常是短句，几乎没有上下文信息，很少有公共带注释的语料库。另一方面，粗粒度命名实体的类型不能提供足够的信息来理解问题。在本文中，我们提出了一种使用远程监督方法来解决这两个问题的新颖模型。首先，我们使用网络搜索来获取更多相关信息。其次，我们提出一个贪婪的n元语法算法来提取实体提及。最后，我们使用kNN进行分类，并获得可与DBpedia链接的实体提及相结合的精细实体类型。实验结果表明，我们的模型优于公共数据集中的各种最新系统-TREC。

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