Natural Language Searching
Revision as of 14:17, 20 August 2010
Natural Language searching uses a relevance ranking to locate documents that are a good match your search terms. It uses a complex algorithm that takes into account factors such as the number of documents that include your search terms and the number of times those terms appear in each document. This is a very different approach from Boolean Searching, which matches your search criteria exactly to documents in the collection.
Natural Language Searching used on two forms in LexisNexis Academic
- Search the News widget on the Easy Search form
- Power Search form (as an option)
Using Natural Language
The natural language feature works best when you:
- Need to research general or conceptual issues, rather than very specific topics
- Don't know much about an issue except for a few basic terms
- Are researching a complex issue and can't construct an effective search using terms and connectors
- Don't feel comfortable writing search requests using terms and connectors
- Want to supplement a terms and connectors search to ensure thorough results
Use Boolean Searching if you want a document from a specific source or source type, from a particular date range, or that uses your search terms in a particular way.
Developing a Natural Language Search
Note: Date restrictions, fielded searching, Boolean connectors such as AND and OR, and wildcard characters such as ! and * are not valid in natural language searches.
To develop a natural language search, use terms that you might use when describing your research topic to another person. Then select the most important terms and phrases, and enter them in any order. To find articles about efforts in the fast food industry to use recyclable packaging, you might use this search:
recycle package "fast food" trash
Enter phrases in quotation marks, like "fast food" to get an exact match. Entering the terms without quotation marks could return documents where they are in different sentences or a different order -- "the food was delivered on a fast train.