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Entity Linking of Artists Names in Japanese Music Articles

机译：在日语音乐文章中链接艺术家名称的实体

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摘要

The opportunity to read music articles and blogs on the Web to get music information is more and more increasing. However, hyperlinks to artist information do not often exist in such articles, and it is a troublesome task for the reader to look it up online. In this paper, in order to make it easy to look up artist information in music articles, we propose a method to extract entities such as artist names in music articles in Japanese, and to perform entity linking which links from artist entities to artist information automatically. The method consists of two phases. First, we extract artist names in music articles. An artist name is a named entity, and it is necessary to distinguish artist names from other named entities, such as personal names of non-artists, place names, organization names, etc. In order to achieve it, we prepare training data of music articles in which artist names are manually tagged, and extract artist names using Support Vector Machine (SVM). Next, we choose a web page of artist information for each extracted artist name. We use Wikipedia as the source of artist information to verify the usefulness of the proposed method, we conducted evaluation experiments using cross-validation. For the experiments, we used 35 Japanese music articles and extracted artist names manually from the articles, and used them for cross-validation. We achieved the recall of 0.2788, precision of 0.7530, and F-measure of 0.4070. We also conducted an experiment to find correct artist information from Wikipedia using edit distance between extracted artist names from ten music articles and Wikipedia titles. We achieved the correct rate of 0.8740 in linking correct Wikipedia articles for artist names.
机译：有机会阅读Web上的音乐文章和博客，以获得音乐信息越来越多。但是，艺术家信息的超链接通常不会存在于此类文章中，并且对于读者来说是一个麻烦的任务，让读者在线查找。在本文中，为了让您在音乐文章中查找艺术家信息，我们提出了一种方法来提取日语音乐文章中的艺术家名称等实体，并执行从艺术家实体自动到艺术家信息的联系。该方法包括两个阶段。首先，我们在音乐文章中提取艺术家名称。艺术家名称是一个命名实体，有必要将艺术家名称与其他命名实体区分开，例如非艺术家，名称，组织名称等的个人名称，我们准备音乐的培训数据手动标记艺术家名称的文章，并使用支持向量机（SVM）提取艺术家名称。接下来，我们为每个提取的艺术家名称选择一个艺术家信息的网页。我们使用维基百科作为艺术家信息来源来验证所提出的方法的有用性，我们使用交叉验证进行了评估实验。对于实验，我们使用了35篇音乐文章并从文章中手动提取艺术家名称，并使用它们进行交叉验证。我们达到了0.2788的召回，精度为0.7530，以及0.4070的F-Peajion。我们还使用来自10音乐文章和维基百科标题的提取艺术家名称之间的编辑距离来查找来自维基百科的正确艺术家信息的实验。我们在将正确的维基百科文章联系起来，我们实现了0.8740的正确速率。

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