Hi Mark I've played with Shingles recently in some auto-categorisation work where my starting assumption was that multi-word terms will hold more information value than individual words and that phrase queries on seperate terms will not give these term combos their true reward (in terms of IDF) - or if they did compute the true IDF, would require lots of disk IO to do this. Shingles present a conveniently pre-aggregated score for these combos. Looking at the results of MoreLikeThis queries based on a shingling analyzers the results I saw generally seemed good but did not formally bench mark this against non-shingled indexes. Not everything was rosy in that I did see some tendency to over-reward certain rare shingles (e.g. a shared mention of "New Years Eve Party" pulled otherwise mostly unrelated news articles together). This led me to look at using the links in resulting documents to help identify clusters of on-topic and potentially off-topic results to tune these discrepancies out but that's another topic. BTW, the Luke tool has a "Zipf" plugin that you may find useful in examining index term distributions in Lucene indexes..
Cheers Mark ________________________________ From: Mark Bennett <mbenn...@ideaeng.com> To: java-...@lucene.apache.org Sent: Fri, 10 September, 2010 1:42:11 Subject: Relevancy, Phrase Boosting, Shingles and Long Tail Curves I want to boost the relevancy of some Question and Answer content. I'm using stop words, Dismax, and I'm already a fan of Phrase Boosting and have cranked that up a bit. But I'm considering using long Shingles to make use of some of the normally stopped out "junk words" in the content to help relevancy further. Reminder: "Shingles" are artificial tokens created by gluing together adjacent words. Input text: This is a sentence Normal tokens: this, is, a, sentence (without removing stop words) 2+3 word shingles: this-is, is-a, a-sentence, this-is-a, is-a-sentence A few questions on relevance and shingles: 1: How similar are the relevancy calculations compare between Shingles and exact phrases? I've seen material saying that shingles can give better performance than normal phrase searching, and I'm assuming this is exact phrase (vs. allowing for phrase slop) But do the relevancy calculations for normal exact phrase and Shingles wind up being *identical*, for the same documents and searches? That would seem an unlikely coincidence, but possibly it could have been engineered to intentionally behave that way. 2: What's the latest on Shingles and Dismax? The low front end low level tokenization in Dismax would seem to be a problem, but does the new parser stuff help with this? 3: I'm thinking of a minimum 3 word shingle, does anybody have comments on shingle length? Eyeballing the 2 word shingles, they don't seem much better than stop words. Obviously my shingle field bypasses stop words. But the 3 word shingles start to look more useful, expressing more intent, such as "how do i", "do i need" and "it work with", etc. Has there been any Lucene/Solr studies specifically on shingle length? and finally... 4: Is it useful to examine your token occurrences against a Power-Law log-log curve? So, with either single words, or shingles, you do a histogram, and then plot the histogram in an X-Y graph, with both axis being logarithmic. Then see if the resulting graph follows (or diverges) from a straight line. This "Long Tail" / Pareto / powerlaw mathematics were very popular a few years ago for looking at histograms of DVD rentals and human activities, and prior to the web, the power law and 80/20 rules has been observed in many other situations, both man made and natural. Also of interest, when a distribution is expected to follow a power line, but the actual data deviates from that theoretical line, then this might indicate some other factors at work, or so the theory goes. So if users' searches follow any type of histogram with a hidden powerlaw line, then it makes sense to me that the source content might also follow a similar distribution. Is the normal IDF ranking inspired by that type of curve? And *if* word occurrences, in either searches or source documents, were expected to follow a power law distribution, then possible shingles would follow such a curve as well. Thinking that document text, like many other things in nature, might follow such a curve, I used the Lucene index to generate such a curve. And I did the same thing for 3 word tokens. The 2 curves do have different slopes, but neither is very straight. So I was wondering if anybody else has looked at IDF curves (actually non-inverted document frequency curves) or raw word instance counts and power law graphs? I haven't found a smoking gun in my online searches, but I'm thinking some of you would know this. -- Mark Bennett / New Idea Engineering, Inc. / mbenn...@ideaeng.com Direct: 408-733-0387 / Main: 866-IDEA-ENG / Cell: 408-829-6513