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From Mark Bennett <>
Subject Re: Relevancy, Phrase Boosting, Shingles and Long Tail Curves
Date Fri, 10 Sep 2010 17:44:31 GMT
Thanks Mark H,

Maybe I'll look at MLT (More Like This) again.  I'll also check out zipf.

It's claimed that Question and Answer wording is different enough for
generic text content that different techniques might be indicated. From what
I remember:
1: Though nouns normally convey 60% of relevancy in general text, Q&A
content is skewed a bit more towards verbs.
2: Questions may contain more noise words (though perhaps in useful
3: Vocabulary mismatch of Interrogative vs. declarative / narrative (Q vs A)
4: Vocabulary mismatch of novices vs experts (Q vs A)

It was item 2 that I was hoping to capitalize on with NGrams / Shingles.

Still waiting for the relevancy math nerds to chime in about the log-log and
IDF stuff ... ;-)

I was thinking a bit more about the math involved here....

What is the "best practices" formula for determining above average
correlations of adjacent terms, beyond what random chance would give. So you
notice that "white" and "house" appear next to each other more than what
chance distribution would explain, so you decide it's an important NGram.

The "noise floor" isn't too bad for the typical shopping cart items
You analyze the items present or not present in 1,000 shopping cart
    If grocery items were completely independent then "random" level is just
the odds of the 2 items multiplied together:
        1,000 shopping carts
        200 have cereal
        250 have milk
    chance of
        cereal = 200/1,000 = 20%
        milk = 250/1,000 = 25%
    IF independent then
        P(cereal AND milk) = P(cereal) * P(milk)
        20% * 25% = 5%
        So 50 carts likely to have both cereal and milk
        And if MORE than 50 carts have cereal and milk, then it's worth
The classic example is diapers and beer, which is a bit apocryphal and NOT
expected, but I like the breakfast cereal and milk example better because it
IS expected.

Now back to word-A appearing directly before word-B, and finding the base
level number of times you'd expect just from random chance.

Although Lucene/Luke gives you total word instances and document counts,
what you'd really want is the number of possible N-Grams, which is affected
by document boundaries, so it gets a little weird.

Some other differences between the word-A word-B calculation vs milk and
1: I want ordered pairs, "white" before "house"
2: A document is NOT like a shopping cart in that I DO care how many times
"white" appears before "house", whereas in the shopping carts I only cared
about present or not present, so document count is less helpful here.

I'm sure some companies and PHD's have super secret formulas for this, but
I'd be content to just compare it to baseline random chance.

Mark B

Mark Bennett / New Idea Engineering, Inc. /
Direct: 408-733-0387 / Main: 866-IDEA-ENG / Cell: 408-829-6513

On Fri, Sep 10, 2010 at 3:17 AM, mark harwood <>wrote:

> 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 <>
> *To:*
> *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. /
> Direct: 408-733-0387 / Main: 866-IDEA-ENG / Cell: 408-829-6513

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