# Patents Phrase to Phrase Semantic Matching Dataset

arXiv: https://arxiv.org/abs/2208.01171 · HTML: https://mlsanity.com/paper/2208.01171/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)

## Built on by (most cited first, 3 of 3)

- [PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation](https://mlsanity.com/paper/2505.19347/) (2025)
- [Logic Mill -- A Knowledge Navigation System](https://mlsanity.com/paper/2301.00200/) (2022)
- [Extracting Text Representations for Terms and Phrases in Technical Domains](https://mlsanity.com/paper/2305.15867/) (2023)

## Abstract (arXiv)

There are many general purpose benchmark datasets for Semantic Textual Similarity but none of them are focused on technical concepts found in patents and scientific publications. This work aims to fill this gap by presenting a new human rated contextual phrase to phrase matching dataset. The entire dataset contains close to 50,000 rated phrase pairs, each with a CPC (Cooperative Patent Classification) class as a context. This paper describes the dataset and some baseline models.