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Full Text Search: The Key to Better Natural Language Queries

Note that using the word breaker of the most complex language in a language family does not guarantee perfect indexing of every language in the family. Corner cases might exist in which the most word breaker cannot correctly handle text written in another language.

In the above example, we also introduced the rather advanced annotation . This annotation refers to a custom implementation of a suitable implementation for the above example would have to return either “de” or “en” and this would allow the entity to be processed with the corresponding Analyzer depending on its language property:The full-text engine is much more impressive when using the other Query types it can generate, and combine them:

Statistics such as can vary widely. For example, if a catalog has 2 billion rows in the master index, then one new document is indexed into an in-memory intermediate index, and ranks for that document based on the number of documents in the in-memory index could be skewed compared with ranks for documents from the master index. For this reason, it is recommended that after any population that results in large number of rows being indexed or re-indexed the indexes be merged into a master index using the ALT

The deficiencies of free text searching have been addressed in two ways: By providing users with tools that enable them to express their search questions more precisely, and by developing new search algorithms that improve retrieval precision.

A natural language search interprets the search string as a phrase in natural human language (a phrase in free text). There are no special operators. The stopword list applies. In addition, words that are present in 50% or more of the rows are considered common and do not match. For large data sets, it is much faster to load your data into a table that has no index and then create the index after that, than to load data into a table that has an existing index.

The diagram at right represents a low-precision, low-recall search. In the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results. Relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. In the example only 1 relevant resu

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