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How Search Really Works: Relevance (2) - Vector Space
by Ruud Hein.
This post is part of an ongoing series: How Search Really Works .
Previously: Relevance (1)
Another way we can assess the relevance of a document is by term weighting .
From the keyword density myth we know that true term weighting is done collection wide.
By looking at the number of documents in the index that a term appears in we can make a measurement of information: how good, how special… how meaningful is this word?
The word the would not be special at all, appearing in way too many documents. Its worth would be close to zero.
But klebenleiben (" the reluctance to stop talking about a certain subject " …)would be very special indeed! Because it appears in only 18 documents among millions, its worth, its weight, would automatically be very high.
The measure is called inverse document frequency.
This measure is our weight; it is what we use to judge the relevance of a document with.
Term Frequency Times
We do so by counting the number of times a word appears in a document. We normalize that count; we adjust it so that the length of a document doesn't matter that much anymore.
We then multiply it by our weight measurement: TF x IDF. Term Frequency times Inverse Document Frequency .
In other words, a high count of a rare word = a high score for that document, for that word . But… a high count of a common word = not so high score for that document, for that word .
Vectors
A vector is a line of a certain length into a certain direction.
Both the length and the direction of the line represent important information.
Vectors enable us to represent, to talk about, size and direction when position is irrelevant. Wind speed, velocity, force, acceleration; all these are good candidates to be represented as a vector.
TFxIDF scores are perfectly suited to be represented as vectors.
Vector Space
Think of the words that make up our index as axes of a space.
Of course in a real index this space would consists of thousands upon thousands of axes…
Documents as Vectors
For each word in our document we can draw a line (vector) which shows its TFxIDF score for a certain term.
Queries as Vectors
Every word in a query can also be shown as a vector.
By looking at documents that are "near" our query we can rank (sort) documents in our result set.
TFxIDF Vector Space Ranking
If a document is close to our query it answers our query.
But better yet: documents close to ours are similar documents . They're talking about roughly the same thing.
This makes TFxIDF vector space ranking extremely useful to find sets of similar documents through "closeness".
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As posted in How Search Really Works on April 11, 2008.
7 Responses so far: 4 comments and 3 trackbacks
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[...] That said, today's post is in reaction to the article at http://www.searchenginepeople.com/blog/how-search-really-works-relevance-2-vector-space.html [...]
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[...] Others have claimed that it is not possible to evaluate the IDF of a phrase. Even some that plan to teach IR have claimed that calling log(N/n) "inverse document frequency" is an "insult to students". Before making a fool of themselves they should read Robertson and Sparck Jones legacy papers on the topic. [...]
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Hi Rudd,
Excellent post as usual. It is important to mention that vector space model for ranking is not currently practical for the top search engines due to the size of their index (and the corresponding size of the document vectors). While they use huge matrices for computing the importance of the links (PageRank), the process is done offline and is query-independent. Computing such vectors are query time would be prohibitively expensive in times and resources.
Cheers
Good indeed to point that out. Doing any of this at run time is extremely costly. There are cost reducing procedures; working with top N documents or leader/follower samples.
Yet I too think that this isn't used at run time (read: query time) because the TFxIDF vector space model is geared towards words. The IDF of a words is computed; not of phrases. All in all it doesn't deliver enough bang for its buck.
Worse: it's typically a model for a clean index. Boosting TF for a high IDF word is too easy when you have search access to the whole collection.
It's interesting though to see how this model can find related documents.
As usual Ruud this is a great post. It's always interesting to learn the inner workings of an SE
An excellent analysis of how to weight terms by their frequency. But I doubt that the two dimensional space is enough to represent the complexity needed to maintain an index of millions of documents.