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

arXiv: https://arxiv.org/abs/2210.06023 · HTML: https://mlsanity.com/paper/2210.06023/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Document Embedding with Paragraph Vectors](https://mlsanity.com/paper/1507.07998/) (2015)
- [Minimally Supervised Categorization of Text with Metadata](https://mlsanity.com/paper/2005.00624/) (2020)

## Built on by (most cited first, 7 of 7)

- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [Efficient Domain Adaptation of Sentence Embeddings Using Adapters](https://mlsanity.com/paper/2307.03104/) (2023)
- [AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge](https://mlsanity.com/paper/2307.07851/) (2023)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (2025)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing](https://mlsanity.com/paper/2404.01443/) (2024)

## Abstract (arXiv)

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.