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首页> 外文会议>2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications >Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri
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Subject Classification of Learning Resources Using Word Embeddings and Semantic Thesauri

机译：利用词嵌入和语义叙词表对学习资源进行学科分类

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摘要

Open Educational Resources (OERs) are often scattered among various sources and may follow different metadata schemata. In addition, they may not include exhaustive annotations; even worse, their subject characterization, if any, may be represented by arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. To address this issue, in this paper we propose a twofold method based on two seemingly disjoint technology stacks: machine learning and the semantic web. First, OERs harvested from various repositories are assigned subject terms from a formal, standard thesaurus for a domain of interest, by discovering the semantic matches of the harvesting keyword within the thesaurus ontology. Then, we use word embeddings to represent an item's metadata and compute its similarity with the thesaurus keywords. These word embeddings are learned by a doc2vec model that has been trained with already annotated corpora from the biomedical domain. By combining both worlds, we show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold.
机译：开放式教育资源（OER）通常散布在各种资源中，并且可能遵循不同的元数据模式。此外，它们可能不包含详尽的注释；更糟糕的是，它们的主题特征（如果有的话）可能用任意的即席关键字代替标准的受控词汇表来表示，这一事实扩大了搜索空间并妨碍了互操作性。为了解决这个问题，本文提出了一种基于两种看似脱节的技术栈的双重方法：机器学习和语义网。首先，通过发现词库本体中收获关键字的语义匹配，从正式的标准词库中为感兴趣的领域分配从各个存储库中获取的OERs主题词。然后，我们使用词嵌入来表示项目的元数据，并计算其与词库关键字的相似度。这些词的嵌入是通过doc2vec模型学习的，该模型已使用来自生物医学领域的已注解语料库进行了训练。通过将两个世界结合起来，我们表明可以产生一组超出一​​定相似性阈值的合理主题建议。

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