Wednesday, March 2, 2016

Searching legal databases more efficiently

Simply entering search terms into a legal database search box in the same way you might enter terms into Google is called Natural Language searching. Natural Language searching is the method of searching with which people are probably most familiar. Unfortunately while it is often the easiest way to do a search it may not be the best way. Terms & Connectors searching is an alternative way to use the search box in Westlaw, Lexis Advance, and Bloomberg Law which can yield more efficient results.

Here are the links to the descriptions of various types of connectors available on Westlaw, Lexis Advance, and Bloomberg Law.

I will not render exhaustive descriptions of each connector and their usage here but rather I will focus on using just one, the proximity connector.  This connector is common to all three of the major commercial databases (as well as several others).  Here, using Westlaw, we will compare some results between it and natural language searching.  This will show you how using the proximity connector can yield more efficient results.

Syntax


A proximity search can be structured like this:

word1 /x word2

This search would find all documents in the database where word1 is within x number of words of word2.

Real property law example: ground lease in Massachusetts




Before starting this search I limited the jurisdiction to Massachusetts.  Entering ground lease in the search box as a natural language search results in 1,730 cases and 838 forms because the search algorithm picks up all cases which have both the words ground and lease.  Using the proximity search ground /1 lease puts those two words next to each other in the document and results in only 54 cases and 408 forms.   So if you have a task which involves ground leasing in Massachusetts you will have much less extraneous material to sort through by using a proximity connector.

Contract law example: purchase agreement in Connecticut




For this example I limited the jurisdiction to Connecticut.  Entering purchase agreement into the search box as a natural language search resulted in 6,697 cases and 2,088 forms.  Using the proximity search purchase /2 agreement returns 1,282 cases and 1,163 forms. Here again, using a proximity connector will return more targeted material.

The tip of the iceberg


These examples are only the tip of the iceberg when it comes to efficient searching because we have only used one type of connector.  If we can increase the efficiency of searching this much using only a single proximity connector as opposed to natural language searching imagine what we can do if we apply several different types of connectors together to a search.  For more information about Terms and Connectors searching and other methods to increase your research productivity, please stop by the reference desk.


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