“Patent documents typically use legal and highly technical language, with context-dependent terms that may have meanings quite different from colloquial usage and even between different documents. The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over one hundred million patent documents can be tedious and result in many missed results due to the broad and non-standard language used. For example, a “soccer ball” may be described as a “spherical recreation device”, “inflatable sportsball” or “ball for ball game”. Additionally, the language used in some patent documents may obfuscate terms to their advantage, so more powerful natural language processing (NLP) and semantic similarity understanding can give everyone access to do a thorough search.
The patent domain (and more general technical literature like scientific publications) poses unique challenges for NLP modeling due to its use of legal and technical terms. While there are multiple commonly used general-purpose semantic textual similarity (STS) benchmark datasets (e.g., STS-B, SICK, MRPC, PIT), to the best of our knowledge, there are currently no datasets focused on technical concepts found in patents and scientific publications (the somewhat related BioASQ challenge contains a biomedical question answering task). Moreover, with the continuing growth in size of the patent corpus (millions of new patents are issued worldwide every year), there is a need to develop more useful NLP models for this domain.
Today, we announce the release of the Patent Phrase Similarity dataset, a new human-rated contextual phrase-to-phrase semantic matching dataset, and the accompanying paper, presented at the SIGIR PatentSemTech Workshop, which focuses on technical terms from patents. The Patent Phrase Similarity dataset contains ~50,000 rated phrase pairs, each with a Cooperative Patent Classification (CPC) class as context. In addition to similarity scores that are typically included in other benchmark datasets, we include granular rating classes similar to WordNet, such as synonym, antonym, hypernym, hyponym, holonym, meronym, and domain related. This dataset (distributed under the Creative Commons Attribution 4.0 International license) was used by Kaggle and USPTO as the benchmark dataset in the U.S. Patent Phrase to Phrase Matching competition to draw more attention to the performance of machine learning models on technical text. Initial results show that models fine-tuned on this new dataset perform substantially better than general pre-trained models without fine-tuning….”
