Natural Language Searches
In addition to Boolean searching, some electronic research services allow you to simply enter research questions in “plain English.” The literature characterizes this alternative search technique in various ways including “natural language,” “associative language,” “probabilistic,” “relevance,” and “statistical” searching. Some tax research services have special names for their systems of natural language searching. For instance, LEXIS calls its natural language algorithm Freestyle, while Westlaw dubs its program WIN (Westlaw Is Natural).
Researchers without “Boolean prowess” may formulate ineffective or inefficient Boolean search requests and be dissatisfied at times with their results. A godsend to those never fully embracing Boolean logic, natural language searching sometimes produces surprisingly good results. Thus, the natural language approach can increase the effectiveness and efficiency of researchers who have not mastered Boolean logic.
But researchers long accustomed to “Boolean speak” can benefit from this alternative approach too. Well-constructed Boolean searches occasionally miss relevant documents or pull too many irrelevant documents. Though natural language searches often do not outperform well-constructed Boolean searches, they do in some cases. So, even Boolean diehards find that natural language searching increases effectiveness and efficiency sometimes.
Understanding Natural Approach
To know when natural language searching might be useful or appropriate, you must understand the way the approach works. First, researchers enter a research question or a string of keywords, ignoring syntax and Boolean connectors. They focus instead on expressing research issues completely (i.e., including all the keywords and concepts). In fact, they need not express the research issue as a question at all; entering a string of keywords that would appear in a well-written research issue works just as well. Second, the system requests researchers to select synonyms and, if they wish, designate some keywords as mandatory. (In effect, designating a keyword as mandatory instructs the system to precede it with AND rather than OR.) Researchers also can restrict searches, for instance, by date or court.
Deciding to Go Natural
How effective and efficient is natural language versus Boolean searching? The jury is still out on this question. To a large extent, the verdict depends on the researcher’s familiarity with Boolean syntax and strategies (see “Boolean Searches” lesson). Boolean neophytes often perform better going natural. But whether a Boolean novice or guru, researchers can more appropriately evaluate natural search results if they recognize and appreciate the approach’s limitations and strengths.
Placing too much reliance on natural language techniques can provide misleading search results. Since the mathematical algorithm (rather than the researcher) makes many choices, less precise results occur in some cases. Consider the following:
Notwithstanding these objections, natural language searches sometimes return better results than Boolean requests. Going natural tends to work reasonably well (perhaps even better than Boolean logic) in the following situations:
Following up a Boolean search with a natural language search (or vice versa) often retrieves relevant documents the initial search did not. The reason is simple. Natural language searches emphasize the relative importance of keywords while Boolean places more emphasis on relationships between keywords. Especially when you are unfamiliar with the database, conducting a search using both Boolean and natural approaches can yield better results than relying only on one technique. A similar approach involves locating a highly relevant document through a Boolean search and then requesting other similar documents. (When using LEXIS, this latter function is called “More Like This” or simply “More.”) In effect, requesting similar documents begins a natural language search based on keywords appearing in the Boolean-retrieved model document.
Individuals can begin their electronic research within traditional tax services (e.g., CCH’s Standard Federal Tax Reporter and RIA’s Federal Tax Coordinator 2d) and then follow hyperlinks to primary sources of the tax law such as rulings and cases. Some electronic research services also allow individuals to follow a reverse process. That is, they can begin their Boolean or natural language search in primary source materials. After locating a particularly relevant ruling or case, they follow a hyperlink back to one of the traditional tax services to discover similar and often relevant information.