24 Nov 2008, Posted by admin in Google, 0 Comments
I’ve been looking at latent semantic indexing recently, it’s the manner in which Google identifies which words belong to others. Instead of looking at individual words and seeing if they’re present, the engine analyses the entire content of a document to see which other words appear, and groups them. It gives them an idea of which context words should be in. You can see how Google does it easily enough with Google Sets. There was an article that described it a lot better than I could that used to be on the Middlebury College site, and it’s not available there any more so I rescued it and republished it here. All credit goes to the Middlebury site. Enjoy.
LATENT SEMANTIC INDEXING
Taking a Holistic View
Regular keyword searches approach a document collection with a kind of accountant mentality: a document contains a given word or it doesn’t, with no middle ground. We create a result set by looking through each document in turn for certain keywords and phrases, tossing aside any documents that don’t contain them, and ordering the rest based on some ranking system. Each document stands alone in judgement before the search algorithm – there is no interdependence of any kind between documents, which are evaluated solely on their contents.
Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. This simple method correlates surprisingly well with how a human being, looking at content, might classify a document collection. Although the LSI algorithm doesn’t understand anything about what the words mean, the patterns it notices can make it seem astonishingly intelligent.
When you search an LSI-indexed database, the search engine looks at similarity values it has calculated for every content word, and returns the documents that it thinks best fit the query. Because two documents may be semantically very close even if they do not share a particular keyword, LSI does not require an exact match to return useful results. Where a plain keyword search will fail if there is no exact match, LSI will often return relevant documents that don’t contain the keyword at all.
To use an earlier example, let’s say we use LSI to index our collection of mathematical articles. If the words n-dimensional, manifold and topology appear together in enough articles, the search algorithm will notice that the three terms are semantically close. A search for n-dimensional manifolds will therefore return a set of articles containing that phrase (the same result we would get with a regular search), but also articles that contain just the word topology. The search engine understands nothing about mathematics, but examining a sufficient number of documents teaches it that the three terms are related. It then uses that information to provide an expanded set of results with better recall than a plain keyword search.
Ignorance is Bliss
We mentioned the difficulty of teaching a computer to organize data into concepts and demonstrate understanding. One great advantage of LSI is that it is a strictly mathematical approach, with no insight into the meaning of the documents or words it analyzes. This makes it a powerful, generic technique able to index any cohesive document collection in any language. It can be used in conjunction with a regular keyword search, or in place of one, with good results.
Before we discuss the theoretical underpinnings of LSI, it’s worth citing a few actual searches from some sample document collections. In each search, a red title or astrisk indicates that the document doesn’t contain the search string, while a blue title or astrisk informs the viewer that the search string is present.
- In an AP news wire database, a search for Saddam Hussein returns articles on the Gulf War, UN sanctions, the oil embargo, and documents on Iraq that do not contain the Iraqi president’s name at all.
- Looking for articles about Tiger Woods in the same database brings up many stories about the golfer, followed by articles about major golf tournaments that don’t mention his name. Constraining the search to days when no articles were written about Tiger Woods still brings up stories about golf tournaments and well-known players.
- In an image database that uses LSI indexing, a search on Normandy invasion shows images of the Bayeux tapestry – the famous tapestry depicting the Norman invasion of England in 1066, the town of Bayeux, followed by photographs of the English invasion of Normandy in 1944.
In all these cases LSI is ‘smart’ enough to see that Saddam Hussein is somehow closely related to Iraq and the Gulf War, that Tiger Woods plays golf, and that Bayeux has close semantic ties to invasions and England. As we will see in our exposition, all of these apparently intelligent connections are artifacts of word use patterns that already exist in our document collection.