# Neural sentence embedding models for semantic similarity estimation in the biomedical domain

arXiv: https://arxiv.org/abs/2110.15708 · HTML: https://mlsanity.com/paper/2110.15708/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation](https://mlsanity.com/paper/1502.03682/) (2015)

## Built on by (most cited first, 4 of 4)

- [BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)](https://mlsanity.com/paper/2210.11892/) (2022)
- [Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04605/) (2021)
- [Similarity Gates Approve Reversals: A Validity Audit of Embedding-Cosine Thresholds in Agent Systems](https://mlsanity.com/paper/2608.10216/) (2026)
- [A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art](https://mlsanity.com/paper/2205.08740/) (2022)

## Abstract (arXiv)

BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set. RESULTS: With a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distributed Memory algorithm outperforms previous state-of-the-art results achieved on the BIOSSES biomedical benchmark set. Moreover, our proposed supervised model that combines different string-based similarity metrics with a neural embedding model surpasses previous ontology-dependent supervised state-of-the-art approaches in terms of Pearson's r (r=0.871) on the biomedical benchmark set. In contrast to the promising results for the original benchmark, we found our best models' performance on the smaller contradiction subset to be poor. CONCLUSIONS: In this study we highlighted the value of neural network-based models for semantic similarity estimation in the biomedical domain by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarity estimation that depend on the availability of laboriously curated ontologies when evaluated on a biomedical benchmark set. Capturing contradictions and negations in biomedical sentences, however, emerged as an essential area for further work.