Lbl2Vec: An Embedding-based Approach for Unsupervised Document Retrieval on Predefined Topics

Tim Schopf, Daniel Braun, Florian Matthes

2021

Abstract

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∗.

Paper Citation

in Harvard Style

Schopf T., Braun D. and Matthes F. (2021). Lbl2Vec: An Embedding-based Approach for Unsupervised Document Retrieval on Predefined Topics. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 124-132. DOI: 10.5220/0010710300003058

in Bibtex Style

@conference{webist21,
author={Tim Schopf and Daniel Braun and Florian Matthes},
title={Lbl2Vec: An Embedding-based Approach for Unsupervised Document Retrieval on Predefined Topics},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={124-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010710300003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Lbl2Vec: An Embedding-based Approach for Unsupervised Document Retrieval on Predefined Topics
SN - 978-989-758-536-4
AU - Schopf T.
AU - Braun D.
AU - Matthes F.
PY - 2021
SP - 124
EP - 132
DO - 10.5220/0010710300003058