# A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art

arXiv: https://arxiv.org/abs/2205.08740 · HTML: https://mlsanity.com/paper/2205.08740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 110 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://mlsanity.com/paper/1805.09843/) (2018)
- [BioSentVec: creating sentence embeddings for biomedical texts](https://mlsanity.com/paper/1810.09302/) (2018)
- [MedSTS: A Resource for Clinical Semantic Textual Similarity](https://mlsanity.com/paper/1808.09397/) (2018)
- [ML-Net: multi-label classification of biomedical texts with deep neural networks](https://mlsanity.com/paper/1811.05475/) (2018)
- [SECNLP: A Survey of Embeddings in Clinical Natural Language Processing](https://mlsanity.com/paper/1903.01039/) (2019)
- [BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale](https://mlsanity.com/paper/1912.10846/) (2019)
- [Neural sentence embedding models for semantic similarity estimation in the biomedical domain](https://mlsanity.com/paper/2110.15708/) (2021)
- [Insights into Analogy Completion from the Biomedical Domain](https://mlsanity.com/paper/1706.02241/) (2017)
- [Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records](https://mlsanity.com/paper/1909.03044/) (2019)

## Abstract (arXiv)

This registered report introduces the largest, and for the first time, reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most of current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate an unexplored benchmark, called Corpus-Transcriptional-Regulation; (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (6) to bridge the lack of reproducibility resources for methods and experiments in this line of research. Our experimental survey is based on a single software platform that is provided with a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments. In addition, we introduce a new aggregated string-based sentence similarity method, called LiBlock, together with eight variants of current ontology-based methods and a new pre-trained word embedding model trained on the full-text articles in the PMC-BioC corpus. Our experiments show that our novel string-based measure sets the new state of the art on the sentence similarity task in the biomedical domain and significantly outperforms all the methods evaluated herein, except one ontology-based method. Likewise, our experiments confirm that the pre-processing stages, and the choice of the NER tool, have a significant impact on the performance of the sentence similarity methods. We also detail some drawbacks and limitations of current methods, and warn on the need of refining the current benchmarks. Finally, a noticeable finding is that our new string-based method significantly outperforms all state-of-the-art Machine Learning models evaluated herein.