In this paper, we consider the task of retrieving documents with predefined
topics from an unlabeled document dataset using an unsupervised approach. The
proposed unsupervised approach requires only a small number of keywords
describing the respective topics and no labeled document. Existing approaches
either heavily relied on a large amount of additionally encoded world knowledge
or on term-document frequencies. Contrariwise, we introduce a method that
learns jointly embedded document and word vectors solely from the unlabeled
document dataset in order to find documents that are semantically similar to
the topics described by the keywords. The proposed method requires almost no
text preprocessing but is simultaneously effective at retrieving relevant
documents with high probability. When successively retrieving documents on
different predefined topics from publicly available and commonly used datasets,
we achieved an average area under the receiver operating characteristic curve
value of 0.95 on one dataset and 0.92 on another. Further, our method can be
used for multiclass document classification, without the need to assign labels
to the dataset in advance. Compared with an unsupervised classification
baseline, we increased F1 scores from 76.6 to 82.7 and from 61.0 to 75.1 on the
respective datasets. For easy replication of our approach, we make the
developed Lbl2Vec code publicly available as a ready-to-use tool under the
3-Clause BSD license.

本文介绍了一种无监督方法，通过学习嵌入式文档和单词向量，从未标记的文档数据集中检索出预定义主题的文档，并可用于多类文档分类，使用 Lbl2Vec 工具可方便地复制此方法。