John Snow Labs Announces Finance NLP and Legal NLP, Bringing State-of-the-Art Natural Language Processing to New Domains
John Snow Labs, the healthcare AI and NLP company and developer of the Spark NLP library, announced the launch of two new products: Finance NLP and Legal NLP. The two libraries come with a series of new pretrained models and state-of-the-art algorithms, able to carry out Entity Recognition, Relation Extraction, Assertion Status Detection, Entity Resolution, De-identification, Text Classification, and more. Spark NLP is used by 50% of practitioners in the finance industry, signaling a demand for a dedicated offering.
John Snow Labs commands a 59% market share in Healthcare & Life Science, with customers including half of the world’s top 10 pharmaceutical companies and the three largest US healthcare companies, among others. Many of the same challenges within healthcare—highly domain-specific language, stringent privacy and compliance regulations, and a mix of structured and unstructured data—apply to the legal and financial industries. Customers of the new libraries will therefore benefit from widely validated solutions to these challenges.
With more than 335 legal models and 60 finance models, John Snow Labs is powering new and innovative NLP applications in each field. In finance, the software can be used to analyze annual reports, carry out competitive analysis, enhance customer support, and help with mission-driven investments. For example, an investor can validate how the companies they’re exploring apply ESG practices. For legal applications, NLP can be used for contract understanding, summarization, comparison, compliance, and more.
“The highly specific jargon and nuanced semantics in legal and financial documents, paired with the sheer amounts of text these industries generate present a massive opportunity for natural language processing to help automate, simplify, and optimize operations,” said David Talby, CTO, John Snow Labs. “Finance NLP and Legal NLP enable that by providing current state-of-the-art accuracy, a broad set of out-of-the-box models for common use cases, and ease of use building them into production systems.”
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