Natural Language Processing

Natural language processing (NLP) is a critical toolkit for extracting information from unstructured, raw data. Using language- and grammar-specific constructs, it builds on a unique combination of algorithms and AI tools to analyze, extract, and classify human communications from unstructured data such as online reviews, patent claims, physician notes in medical files, insurance claims, and even audio recordings. As a result, it can be used to develop many different types of analyses, including predictive models, across a number of sectors.

Advances in generative AI (GenAI) further enhance our NLP capabilities by enabling more sophisticated text analysis, summarization, and knowledge extraction at scale. These innovations allow us to process vast amounts of unstructured data more efficiently, improving both speed and accuracy in decision making.

We have used NLP and GenAI-driven techniques to help clients:

• Search through reams of filings and online product reviews to reveal which features were considered relevant to consumers in patent infringement and consumer protection cases
• Transcribe and analyze audio recordings to identify and categorize topics of discussion, uncover key themes, and measure their impact on corresponding stock prices
• Identify and interpret relevant language in trading-related chat messages, contextualizing trading activity in investigations of market conduct and price manipulation
• Collect, cluster, and analyze information from various unstructured financial text sources to find relationships between certain language patterns and conduct in financial markets
• Examine detailed online booking data such as pricing or specific characteristics in order to measure the impact of constraints – or their removal – on competition and consumer welfare
• Uncover issues not captured by traditional patient-reported outcome instruments, including insights from social media and online patient data on medical conditions and treatments
• Efficiently conduct literature reviews by organizing large datasets of scientific articles, ranking abstracts based on expected relevance, and highlighting shifts in research topics over time
• Develop methods to identify and standardize medical terminologies such as disease names from unstructured medical data in China’s electronic medical record system
• Identify causally related medical device adverse event reports in the US Food and Drug Administration's narrative-based Manufacturer and User Facility Device Experience database

By combining NLP with emerging GenAI capabilities, we continue to push the boundaries of text analysis, enabling more powerful, scalable, and efficient solutions for complex data challenges across industries.