Paul, I'm glad to hear you are doing this -- it sounds like a great project. After sending the message below, I was thinking about the possibility of a flexible scoring architecture that would allow application developers to easily customize scoring to their needs. One simple thing I'm missing in the current Similarity mechanism is a parameter that identifies the field for which the various factors are being computed. For various reasons, I'd like to use different computations for different fields in my application.
Probabilistic scoring models generally use some kind of training or learning mechanism to induce optimal coefficients and additive factors for combining a variety of score components (including tf, idf, field length and others -- e.g., OKAPI uses a term related to but different from idf: the ratio of number of docs not containing a term to the number of docs that do contain the term). It would be great to have a scoring mechanism that defined an extensible library of standard score components and provided the flexibility to combine them arbitrarily, and to use training to induce an optimal combining function. The probabilistic models have a couple significant advantages: 1. An application developer can optimize relevance by using training/learning techniques. 2. Scores are true probabilities that reflect the likelihood a result is relevant. This allows absolute comparability across searches and various thresholding, segregating and other UI devices to communicate the quality of results to the user. I would love to see a flexible scoring mechanism to go along with a more structured query mechanism in Lucene, and would be interested in helping to create this. Within this mechanism, the default scoring formula should be one of the best empirically tested and tuned variations, e.g., Salton's final SMART formula or Robertson's OKAPI. Re. combining idf with structured query languages, I believe a number of people do this. Let me describe what we did at InQuira briefly. (I use the past tense here only because I've left the company.) The user query language was either traditional keyword (with standard phrase, +/-, etc. operators) or a natural language question. We used a front-end rule system to rewrite the user query into a structured back-end query language. Those rules contain many heuristics, especially relative to the natural language queries (e.g., "when" translates into a date search, "how much" into a quantity or currency search, etc.). The back-end query language has the normal Boolean, distance, etc., operations, along with some less common ones (e.g., we had an ontology, indexed concepts, and could do concept searches). Parts of the scoring were fixed while other parts were flexible: e.g., there were three macro-components that could be weighted per-application or even per-query. The macro-components are excerpt-relevance (how likely a sentence of paragraph is to precisely answer the question), document relevance (tfidf is a major factor here, along with page rank, etc.), and recency. Re. per-query weighting, consider that current events oriented queries generally emphasize recency, topical queries emphasize document relevance and authority (tfidf and page rank), and specific questions emphasize excerpt relevance. Through empirical testing we found that the optimal relationship between the macro-score components was one of tie-breaking. E.g., tfidf was a good tie-breaker among results that appeared to have equally good excerpts. I don't think Lucene should go in the question-answering direction, but do think it should support a range of useful scoring components that can be tuned and combined to yield great relevancy. E.g., page rank is an extremely valuable factor in web search, and in any domain where this is a rich hyper-link structure. Having the flexibility in Lucene to easily incorporate this into scoring for domains where it is available and useful would be great. If you are interested in creating a flexible scoring architecture to go along with your new query mechanism and I could help, please let me know. Chuck > -----Original Message----- > From: Paul Elschot [mailto:[EMAIL PROTECTED] > Sent: Sunday, October 17, 2004 3:15 AM > To: Lucene Developers List > Subject: Re: idf^2 > > Chuck, > > I'm working on a more structured query language with > the usual explicit boolean and distance operators. > > For the scoring I'm still in mostly the design stage. > I'm considering to drop the idf altogether for several reasons. > > One reason is the issue of very low frequency terms (typo's in full > text of articles and pdf's for example) that get into queries via prefix > terms and fuzzy terms. This is also occasionaly reported in the IR > literature > but I don't have a reference handy. > > Another reason is that the users of a structured query language > have their own ideas about the importance of terms. They take > the effort to relate their terms with the operators, and the > concept of idf doesn't seem to fit in there. > > Also, I'm not aware of research results on the combination of > idf, structured query languages and full text search. I have the > impression that idf is good for text like queries on abstracts, > and that the document length and document term > frequency are more important than idf for searching in full text. > > Finally, in my case, typically a classification based limitation is > used that reduces the searched documents to about 0.5% - 1.5% > of the total number of documents available, leaving the idf less > accurate. > > Any comments? > > ... > >However, Lucene is a very good > >search engine and it seems right that it would have a "best of class" > >scoring formula out of the box. > > I fully agree. It's also a very good environment for > designing/implementing > another query language, although that by itself turns out to be > harder than I anticipated... > > > Regards, > Paul Elschot > > > On Sunday 17 October 2004 02:22, Chuck Williams wrote: > > Doug Cutting wrote: > > > If someone can demonstrate that an alternate formulation produces > > > superior results for most applications, then we should of course > > > > change > > > > > the default implementation. But just noting that there's a factor > > > > which > > > > > is equal to idf^2 in each element of the sum does not do this. > > > > I researched the idf^2 issue further and believe that empirical studies > > have consistently concluded that one idf factor should be dropped. > > Salton, the originator of the IR vector space model, decided to drop the > > idf term on documents in order to avoid the squaring. I hear he did > > this after studying recall and precision for many variations of his > > formula. Here is a quote from his Trec-3 paper, which references the > > same comment in his Trec-1 and Trec-2 papers. Note the final sentence: > > > ... > > To allow a meaningful final retrieval similarity, it is convenient to > > use > > a length normalization factor as part of the term weighting formula. > > A > > high-quality term weighting formula for wik, the weight of term Tk > > in query Qi is > > > > wik= [ log(fik) + 1.0) * log(N/nk) ] / > > sqrt(sum(j=1, t) [(log(fij) + 1.0) * log(N/nj)]^2) (1) > > > > where fik is the occurrence frequency of Tk in Qi, N is the > > collection > > size, and nk is the number of documents with term Tk assigned. The > > factor > > log(N/nk) is an inverse collection frequency ("idf") factor which > > decreases as terms are used widely in a collection, and the > > denominator > > in expression (1) is used for weight normalization. This particular > > form > > will be called "ltc" weighting within this paper. > > > > The weights assigned to terms in documents are much the same. In > > practice, > > for both effectiveness and efficiency reasons the idf factor in the > > documents is dropped.[2, 1]" > > > > Similarly, another successful scoring formula that has been extensively > > tuned through empirical studies, OKAPI, uses idf linearly and not > > quadratically. I checked with some friends that are more expert in this > > area than me, Edwin Cooper the founder and Chief Scientist at InQuira, > > and his father Bill Cooper, a pioneer in IR and professor emeritus at > > Berkeley. Both of them report that squaring the idf term seems > > "strange" and is not consistent with the best known scoring formulas. > > > > At InQuira, we did extensive empirical tests for relevance in many > > different domains. Our situation was different as the product retrieves > > specific passages so our model was much more complex (extracting > > sentences or paragraphs from a large corpus that specifically answer a > > natural language question -- e.g. try a question like "what are roth vs > > regular iras" in the search box at www.bankofamerica.com). However, we > > did include document relevance factors including tfidf and did not > > square the idf. None of our testing indicated that would have been an > > improvement, although we did not explicitly try it. > > > ... > > Chuck > > > > > -----Original Message----- > > > From: Doug Cutting [mailto:[EMAIL PROTECTED] > > > Sent: Wednesday, October 13, 2004 9:25 AM > > > To: Lucene Developers List > > > Subject: Re: Contribution: better multi-field searching > > > > > > Paul Elschot wrote: > > > >>Did you see my IDF question at the bottom of the original note? I'm > > > >>really curious why the square of IDF is used for Term and Phrase > > > >>queries, rather than just IDF. It seems like it might be a bug? > > > > > > > > I missed that. > > > > It has been discussed recently, but I don't remember the outcome, > > > > perhaps some else? > > > > > > This has indeed been discussed before. > > > > > > Lucene computes a dot-product of a query vector and each document > > > vector. Weights in both vectors are normalized tf*idf, i.e., > > > (tf*idf)/length. The dot product of vectors d and q is: > > > > > > score(d,q) = sum over t of ( weight(t,q) * weight(t,d) ) > > > > > > Given this formulation, and the use of tf*idf weights, each component > > > > of > > > > > the sum has an idf^2 factor. That's just the way it works with dot > > > products of tf*idf/length vectors. It's not a bug. If folks don't > > > > like > > > > > it they can simply override Similarity.idf() to return sqrt(super()). > > > > > > If someone can demonstrate that an alternate formulation produces > > > superior results for most applications, then we should of course > > > > change > > > > > the default implementation. But just noting that there's a factor > > > > which > > > > > is equal to idf^2 in each element of the sum does not do this. > > > > > > Doug > > > > ... > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
