Digital White Papers

I L T A W H I T E P A P E R | K N O W L E D G E M A N A G E M E N T & M A R K E T I N G T E C H N O L O G I E S 14 H U M A N I Z I N G S E A R C H : H O W N L S M O B I L I Z E S K N O W L E D G E I N S I G H T S & D R I V E S T O O L A D O P T I O N Accessing valuable knowledge is still cumbersome without knowledge management tools that align with how lawyers think and work. This reality underscores the need for a more intuitive, human- centered search technology that bridges the gap between intent and data, enabling lawyers to quickly and accurately find knowledge. NLS: Intuitively Navigate Firm Knowledge NLS is a term becoming more and more common in legal technology spaces. But what does it mean? At its core, NLS is the most accessible, reliable way to navigate large language data sets. It can uniquely mobilize the language data stored in a firm's knowledge management system – instantly connecting lawyers with relevant language in their firm history. NLS leverages natural language processing (NLP) to analyze large amounts of language data through the syntax of everyday language. NLP interprets language conversationally by applying language models and computational linguistics. Its ability to "understand" context, intent, and nuance produces results that align with a searcher's intent. You can imagine that interacting with NLS is akin to asking a human for help navigating an extensive database, say your local librarian. It can intuit, interpret, and learn relevance and context like your librarian. Typically, you would not walk up to a librarian, communicate keywords like "1952" and "web," and expect the librarian to help with your research effectively. This disjunct type of communication may produce some relevant results, but a conversationally posed inquiry is more likely to be effective. For example: "Can you help me find the children's book named something like Wilbur's Web or The Web's Tale, written in 1952?". Like a librarian, NLS can interpret context and relevance, allowing you to engage with the library's content efficiently and reliably. NLS in Today's Search Landscape The risks of an ineffective search system are twofold. They can incorrectly conclude that relevant information does not exist and waste time finding information that is already known to exist. Some legal search methods, including Boolean, lexical or keyword, and semantic search, open the door to multiplying these risks. • Boolean Search: Traditionally, legal tools rely on Boolean search functionality, which demands high precision from the user. A searcher must know document naming conventions, syntax operators, and connectors to find specific contractual language with a Boolean search. The complexity of Boolean syntax compounds quickly, and even minor errors can inadvertently filter out critical information. • Lexical Search: Lexical, or keyword search, broadens the search scope by indexing documents based on relevant words or phrases. Although this method is less syntactically rigid than Boolean searches, it lacks contextual understanding. Lexical searches retrieve words in isolation rather than as part of a cohesive phrase or concept. For example, searching for "contract breach" may return results where "contract" and "breach" appear separately in the document, both present but failing to relate. • Semantic Search: Semantic search, a part of NLS, uses NLP and machine learning to interpret a query. Unlike Boolean and lexical or keyword searches, semantic search captures contextual meaning. However, its inability to capture intent distinguishes it from NLS. When used alone, semantic search risks producing overly broad results by surfacing content beyond the specific query terms, increasing the chances of irrelevant information and potentially burying relevant information in cluttered search results. NLS's Superpowers Understanding the transformative potential of NLS requires exploring how it leverages context in the search process and user experience to surface precise and relevant knowledge. At the most basic level, context awareness is recognizing the words in a query carry a particular intention or are associated with a concept. Unlike Boolean and lexical search, NLS can apply context to refine search results and define search relevance ranking. This combination

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