How Boolean and natural language search uncover different results—and why effective legal researchers use both.
An attorney tasked with a premises liability case types “unsafe condition” into Trellis and gets roughly 14,000 results. Another attorney, researching the same issue in the same state, enters “dangerous condition” and gets more than 41,000 results.
Both attorneys are looking for case materials involving injuries caused by hazards on a property. Yet one uncovers more than three times as many results as the other.
Why?
The answer has less to do with the facts of the case than the vocabulary used to describe them. In this article, we’ll examine how terminology, jurisdictional drafting conventions, and search methodology affect legal research results—and how attorneys can pair Boolean and natural language search to move from broad exploration to precise, defensible research.
Two different ways to find relevant cases
Boolean search works by following instructions.
A query tells the system exactly what to include, exclude, or prioritize. You can require specific terms, combine concepts with AND and OR operators, or specify how closely words must appear together. The results are predictable: run the same query twice and you get the same results.
That consistency is what makes Boolean search so valuable. When you need to apply precise criteria or document your search methodology—it’s hard to beat. But, that precision only extends as far as the terms you choose. Boolean search finds exactly what you ask for, but nothing you don’t.
An attorney looking for slip-and-fall cases involving failure to warn may miss filings that describe the issue differently, whether as a failure to post warning signs, an inadequate warning, or a dangerous condition left unmarked. A Boolean search can’t fill in the missing vocabulary.
Natural language search approaches the problem differently.
Rather than focusing on exact terms, a natural language search (NLS) seeks to understand the meaning behind a query. An attorney can describe an event, a scenario, or a fact pattern, and the system will surface relevant materials that reflect the legal concepts associated with that description.
NLS allows attorneys to begin with facts rather than keywords, surfacing related terminology and search formulations they may not have considered. That makes it especially useful early in the research process, when you’re still learning how courts and litigants characterize an issue.
What this looks like on real court data
A simple search for slip-and-fall cases illustrates how search methods shape search results. In this well-traveled area of law, small differences in terminology can dramatically affect what attorneys find. A quick search through Trellis reveals the scale of those differences.
A problem of varied vocabularies
Let’s start with New York torts cases. A Boolean search for “unsafe condition” AND premises returns 12,214 results. That’s substantial. Now, run the synonyms.
All six terms describe the same basic harm, but the counts vary by a factor of 670. An attorney who searches for “unsafe condition” and stops there may find 12,214 cases, while missing tens of thousands of other potentially relevant cases that use different terminology.
The original Boolean query is precise, but it’s applied to an incomplete vocabulary. This is the gap NLS is built to close, helping attorneys move from a fact pattern to the terminology courts and litigants actually use.
The cross-jurisdiction twist
Here’s where it gets more complicated. Court vocabularies don’t look the same everywhere, and a query built for one state can fail badly in another. Let’s compare results from two separate Boolean searches on Trellis.
The California inversion looks like a litigation trend, suggesting that trip-and-fall cases are significantly more common than slip-and-fall cases across the state. But, the discrepancy is largely a product of drafting conventions.
In Los Angeles—the highest-volume court system in the state—complaints routinely use the boilerplate phrase slip and/or trip and fall to describe premises liability claims. A Boolean search for “slip and fall” only surfaces materials with that exact phrase. As a result, filings that use slip and/or trip and fall are excluded from the results, even though they’re directly on point.
These are the types of patterns NLS catches.
Three research scenarios
The same fact pattern can be categorized under different matter types, described through different terminologies, and expressed using different drafting conventions.
The solution isn’t to run more searches blindly. It’s to use the right search method at the right stage of the research process. Here’s what that looks like in practice.
Fact-pattern research
This is where NLS provides the most value, particularly for researchers working in an unfamiliar practice area or dealing with a claim type they haven’t handled before.
Take a concrete example: a shopper gets detained by store security after being accused of shoplifting. We know the facts, but we may not yet know how the local courts characterize the claim or what terminology appears most often in its filings. Let’s ask.
NLS lets you start where every case starts: with a description of what happened. It then surfaces filings that help you identify the legal theories, claims, defenses, and terminology courts use, so you can build a more precise query.
Outcome-oriented research
Sometimes the goal isn’t to understand a legal issue. It’s to find cases with a specific procedural history or outcome.
A litigator preparing a motion, evaluating a defense, or assessing litigation risk might want to ask:
NLS is often the fastest way to get there. Rather than constructing a complex query that combines causes of action, procedural events, outcomes, and jurisdictional filters from scratch, an attorney can describe the result they’re looking for in plain English. Trellis identifies relevant results and provides a corresponding Boolean query that attorneys can inspect, modify, or refine.
This example shows how NLS understands the conceptual overlaps between related legal terms. An attorney who queries motion to dismiss will also surface demurrer. The system recognizes that the two are doctrinally related—not distinct concepts requiring separate searches. Thus, an attorney who uses one term but not the other still gets the full picture.
Scope-based research
As a case develops, attorneys move from exploration to validation. The challenge shifts from finding relevant materials to deciding exactly which ones belong in the analysis. Let’s return to our retail store example from slip-and-fall litigation.
An NLS might surface cases involving wet floors, dangerous conditions, and premises liability. That’s a strong starting point. But, in your case, the store’s failure to warn is the central theory of liability—a theory that appears in some, but not all, premises liability cases.
This is where Boolean becomes indispensable. If failure-to-warn cases matter, this is where you can include them explicitly. You decide which theories, terms, jurisdictions, and outcomes belong in the result set.
The hybrid workflow in practice
NLS facilitates exploration. Boolean search provides control. The most effective legal research workflows treat them as complementary tools, used in sequence.
Start with NLS to get oriented. Describe the facts, legal issue, or outcome you’re interested in and review the results. Pay attention to the terminology that appears in the cases, the matter types that surface most often, and the procedural events associated with the dispute. You’re building a map of the terminological landscape before you start making precise queries.
Review the Boolean query. On Trellis, users can move directly between NLS and Boolean search, moving seamlessly from an exploratory search to a Boolean query that can be reviewed, adjusted, and documented. Reviewing the Boolean query lets attorneys verify what’s been included, what may be missing, and whether the scope of the search matches your research objective.
Refine the Boolean query as needed. A slip-and-fall query might include “premises liability” and “dangerous condition” but omit concepts that are only sometimes applicable—like “failure to warn” or “negligent maintenance.” The Trellis data above shows just how much volume lives in those adjacent terms. This is where you tailor your query, close the gaps, and document your search methodology.
Closing thoughts
Legal research is rarely a one-query exercise.
The same claim can be described differently across jurisdictions, courts, and filings. Local drafting conventions can distort search results. And related theories can live under entirely different terminologies. A search that feels comprehensive can still leave important cases behind.
Search method matters.
NLS helps attorneys get started. Boolean search helps define the path. One is designed for discovery. The other is designed for control. The most effective legal research doesn’t choose between them. It uses both.
FAQ
The most effective way to find similar cases is to start with the facts rather than legal terminology. Attorneys often know what happened but may not know how courts, opposing counsel, or other litigants have characterized those facts. Searching based on a fact pattern can help uncover relevant causes of action, defenses, procedural issues, and terminology that might otherwise be missed.
Trellis allows attorneys to search using natural language, making it possible to describe a fact pattern in plain English and identify related cases from actual court filings. Researchers can then refine those results using Boolean search to create a more targeted and defensible set of materials.
Yes. Natural language search allows attorneys to search using a description of events, conduct, or circumstances rather than specific legal terms. This can be especially useful when researching unfamiliar issues or when the correct legal terminology is unclear.
On Trellis, attorneys can describe a dispute, procedural posture, or factual scenario and surface cases involving similar concepts. The resulting cases often reveal terminology, causes of action, and related issues that can be incorporated into a more precise search strategy.
When legal terminology is unclear, it’s often best to begin with the facts. Courts and litigants may use terminology that differs from the language an attorney initially has in mind, making keyword-only research more difficult.
Trellis helps bridge this gap through natural language search. By starting with a plain-English description of a case or issue, attorneys can identify how similar matters are characterized in court filings and use that language to build more targeted searches.
Natural language search is often most useful during the early stages of research, when attorneys are exploring a legal issue, identifying terminology, or looking for similar fact patterns. Boolean search is generally better suited for refining results, applying specific criteria, and creating a repeatable research methodology.
Trellis supports both approaches, allowing attorneys to begin with a natural language search and then review, modify, and refine the resulting Boolean query. This makes it easier to move from broad exploration to precise legal research.
Relevant cases are often missed because similar legal issues can be described using different terminology. A search may capture cases that use one phrase while excluding filings that rely on alternative language, even when the underlying facts and legal theories are similar.
Trellis helps attorneys identify related terminology through natural language search and real court filings. By exposing the language commonly used across similar matters, researchers can expand their searches and uncover relevant cases that might otherwise be overlooked.
Attorneys frequently need to locate cases involving particular procedural events, such as denied motions to dismiss, granted summary judgment motions, or specific settlement outcomes. Finding those cases often requires combining legal issues with procedural history.
On Trellis, attorneys can search for desired outcomes using natural language and identify cases with similar procedural developments. Those results can then be refined further using Boolean search and jurisdiction-specific filters.
One of the most effective ways to expand a search is to identify alternative terminology, related legal theories, and different ways courts describe the same issue. Limiting research to a single phrase can unintentionally exclude a large number of relevant cases.
Trellis helps researchers uncover related terminology by analyzing actual court filings and surfacing conceptually similar results. This allows attorneys to broaden their search beyond a single keyword or phrase and discover additional relevant materials.
A strong research workflow often begins with a fact pattern and gradually becomes more focused as relevant terminology and legal theories emerge. The goal is to understand how courts and litigants describe the issue before narrowing the scope of the research.
Trellis supports this workflow by allowing attorneys to start with natural language search and then transition directly into Boolean search. This helps researchers identify relevant concepts first and apply precision later in the process.
Legal concepts are often expressed using multiple terms, phrases, and drafting conventions. Even when two terms describe a similar issue, a search engine may return substantially different result sets depending on the exact language used.
Trellis helps attorneys identify terminology variations by surfacing related concepts and language patterns found in court filings. This can help researchers understand whether important cases may be hiding behind alternative wording.
Courts in different states often develop distinct drafting practices, terminology, and pleading conventions. As a result, a search strategy that performs well in one jurisdiction may overlook relevant cases in another.
Because Trellis provides access to state trial court records across jurisdictions, attorneys can compare how similar claims are described in different courts and adjust their search strategy accordingly.
Many courts and practitioners rely on recurring phrases, templates, and standard pleading language. These conventions can influence which cases appear in a search and which ones remain hidden behind different terminology.
By searching across millions of court filings, Trellis helps attorneys identify common language patterns and drafting conventions that may impact search results. Understanding those patterns can lead to more comprehensive and accurate research.
When a search returns too many cases, the most effective approach is to introduce additional criteria such as specific legal theories, procedural outcomes, jurisdictions, or factual circumstances. The objective is to reduce noise without excluding relevant materials.
Trellis allows attorneys to refine broad search results using Boolean operators, filters, and jurisdiction-specific criteria. This helps researchers move from a large collection of potentially relevant cases to a focused set of materials that directly support their analysis.