# Query expansion and Key word extraction

## Overview

Whoosh provides methods for computing the “key terms” of a set of documents. For these methods, “key terms” basically means terms that are frequent in the given documents, but relatively infrequent in the indexed collection as a whole.

Because this is a purely statistical operation, not a natural language processing or AI function, the quality of the results will vary based on the content, the size of the document collection, and the number of documents for which you extract keywords.

These methods can be useful for providing the following features to users:

- Search term expansion. You can extract key terms for the top N results from a query and suggest them to the user as additional/alternate query terms to try.

- Tag suggestion. Extracting the key terms for a single document may yield useful suggestions for tagging the document.

- “More like this”. You can extract key terms for the top ten or so results from a query (and removing the original query terms), and use those key words as the basis for another query that may find more documents using terms the user didn’t think of.

## Usage

- Get more documents like a certain search hit. This requires that the field you want to match on is vectored or stored, or that you have access to the original text (such as from a database).

  Use `more_like_this()`:

      results = mysearcher.search(myquery)
      first_hit = results[0]
      more_results = first_hit.more_like_this("content")

- Extract keywords for the top N documents in a `whoosh.searching.Results` object. This requires that the field is either vectored or stored.

  Use the `key_terms()` method of the `whoosh.searching.Results` object to extract keywords from the top N documents of the result set.

  For example, to extract five key terms from the `content` field of the top ten documents of a results object:

      keywords = [keyword for keyword, score
                  in results.key_terms("content", docs=10, numterms=5)

- Extract keywords for an arbitrary set of documents. This requires that the field is either vectored or stored.

  Use the `document_number()` or `document_numbers()` methods of the `whoosh.searching.Searcher` object to get the document numbers for the document(s) you want to extract keywords from.

  Use the `key_terms()` method of a `whoosh.searching.Searcher` to extract the keywords, given the list of document numbers.

  For example, let’s say you have an index of emails. To extract key terms from the `content` field of emails whose `emailto` field contains `matt@whoosh.ca`:

      with email_index.searcher() as s:
          docnums = s.document_numbers(emailto=u"matt@whoosh.ca")
          keywords = [keyword for keyword, score
                      in s.key_terms(docnums, "body")]

- Extract keywords from arbitrary text not in the index.

  Use the `key_terms_from_text()` method of a `whoosh.searching.Searcher` to extract the keywords, given the text:

      with email_index.searcher() as s:
          keywords = [keyword for keyword, score
                      in s.key_terms_from_text("body", mytext)]

## Expansion models

The `ExpansionModel` subclasses in the `whoosh.classify` module implement different weighting functions for key words. These models are translated into Python from original Java implementations in Terrier.