Optimizing Document Scoring for Query Retrieval

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1 Optmzng Document Scorng for Query Retreval Brent Ellwen Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng to fnd an optmal scorng functon gven term frequency, document frequency, and query matchng. The accuracy of the results was measured as the greatest average precson of the top ten scored documents. Background Ths project consdered 3 statstcs when scorng a document s relevance to a gven query. These statstcs are common throughout Informaton Retreval: Term Frequency (tf) a measure of the number of tmes that a gven token appears n a document Inverse Document Frequency (df) a measure of the nverse of the number of documents that the token appears n Overlap (coord) a measure of the number of query terms n the document For a typcal nformaton retreval system, one would not use the raw number, but would nstead use a scalng functon of the statstc. One mght use square roots or logs to perform the scalng. The total score s reported as a combnaton of the ndvdual terms. However, t s not generally known what the best scorng functon or combnaton of functons s. In fact, the best scorng functon may be dfferent dependng on the doman of the corpus and any artfacts of the content 1. Expermental Condtons For tranng, testng, and the collecton of results, I used the Cranfeld collecton. Ths corpus conssts of approxmately 14 abstracts and 226 queres all related to aeronautcal engneerng. Each document s scored relevant or not relevant for each of the queres. 1 For example, when tryng to avod spam documents on the nternet, one mght want to severely dampen, or even lmt the tf score. Scorng s calculated as the scaled product of tf, df, and coord. After computng the score for each document, the top 1 most relevant documents are compared aganst the known relevancy values. The precson s the rato of relevant documents to total number of documents returned (1). These precson scores are then averaged and the result s the overall precson of the engne on the queres. The objectve s to maxmze ths overall precson. Learnng Envronment In order to fnd the optmal combnaton of scalng functons, I used the followng parameterzaton functon weghts. Each of the parameters θ are optmzed by the learnng algorthm. tf (as a functon of frequency f) f θ + θ + θ log( f + θ f + θe 1f 2 ) 3 4 df (as a functon of document frequency df and number of documents n) θ + θ / df+ 1+ θ log(/ n df+ 1) + θ n/ df+ + θ ( ) ( ) ( ) df 5 6n coord (as a functon of term matches m and number of query terms q) θ + θ / q+ θ log( m/ q) + θ m q+ θ m 1 11m / 14 total score (product of the parameters) θ 15 tf 16 * 17 * θ df θ coord Each parameter was optmzed accordng the K-fold cross valdaton for varous values of K. It seems obvous that the parameters θ 15, θ 16, and θ 17 are redundant (that s to say ther values could n effect be combned wth the parameters from the groups they correspond to), but early experments showed that they mproved the convergence rate. Later, as normalzed Coordnate Ascent was mplemented, ther purpose was decoupled from the parameters nsde the groups and they became scalng factors for each group as a whole. The default parameters for the query engne, whch are typcal default parameters used throughout the feld of Informaton Retreval, are: tf * (log( n /( df + 1)) + 1) * m / q In other words, θ =θ 3 =θ 5 =θ 7 =θ 11 =θ 15 =θ 16 =θ 17 =1. All others would be set to zero.

2 Bounded Coordnate Ascent The frst algorthm I attempted to optmze the parameters wth was a bounded coordnate ascent model. It s mportant to note that each tranng teraton started wth random values for each of the parameters. I dscovered that f I ntalzed the values to the default values, that the algorthm was unable to learn from the local optmum that the values represent. In ths model, each parameter s optmzed ndvdually accordng to the Newton- Raphson method. At any gven optmzaton step, the values each theta s allowed to take are strctly bounded by -1 and 1. The model was ntalzed wth random values and optmzaton was repeated untl convergence. The results of ths optmzaton were hghly unsatsfactory compared aganst the default confguraton. The followng charts summarze the precson results for a couple tral of k-fold valdaton. For these charts, tranng refers to the queres used as the tranng set and test refers to the queres held out of tranng and used for valdaton. Default values refer to the default confguraton mentoned above. Learned refers to the optmzed parameters Average Precson (K=1) Average Precson (K=2) It s easy to see from these graphs that the optmzed classfer generally performs worse than the default classfer for both tranng and test queres. The optmzed classfer more closely matches, and even out-performs the default classfer when K s large whch suggests that a large number of tranng examples s necessary n order to properly optmze the parameters. Further experments show that even when usng the entre dataset, the optmzed classfer wll typcally under perform the default classfer. An explanaton for the negatve results s that tranng yelded a hgh level of varaton n the parameters across tranng datasets. Ths ndcates the presence of many local optma that the algorthm was unable to recover from. Below s a table whch summarzes the fluctuatons n the values for each theta Standard Devaton for each theta K = 5 K = 1 K = 2 As can clearly be shown n the chart, nearly each value for theta s fluctuatng volently across each of K trals of K-fold cross valdaton. The hgh degree of varaton led me to beleve that the parameters were not convergng to optma close to the global maxmum, but were too hghly nfluence by ther ntal random values. Normalzed Coordnate Ascent The next logcal step n order to ncrease the precson of the optmzed query engne was to add restrctons on the values each parameter could hold. As before, the values had to be ntalzed at random. Frst, I tred restrctng the value to be bounded by and 1, but that dd not provde notceable mprovement. Then, I added the restrcton that, after each optmzaton, all the values be re-normalzed across categores so that they would sum to 1. Naturally, all the values would then be restrcted to the range -1 after optmzaton, but the Newton-Raphson steps would stll be allowed to explore beyond the 1 boundary. Whle ths yelded slghtly better results, the results were stll on average only equal to the default classfer. Gong another step further, I restrcted the parameters to sum to one only wthn specfc groupngs. follows: tf group 5 = The groupngs are enumerated as

3 df group 1 = 6 coord group 14 = 1 total score group 17 = 15 I ran the tranng and testng examples agan and recorded that the fluctuatons n the parameters theta had notceably reduced. Whle t s true that the forced scalng s cause for some scaled reducton, another thng to notce s that several parameters almost never change. The followng chart, as before, summarzes the results Standard Devaton for theta (normalzed) K = 5 K = 1 K = 2 Whle there s some varaton n the data, ths can be explaned by lookng at the raw values for the parameters (whch are too numerous to nclude n ths report). I wll provde an n-depth dscusson for each parameter whch wll provde some reasonng for the behavor of ts optmzed value. Havng succeeded n reducng the varance of the optmzaton, we can now look more closely at the tral results for k-fold cross valdaton. As before, the followng precson charts summarze the behavor for each of K = 5,1, Average Precson (K = 5) (normalzed) Average Precson (K = 1) (normalzed) Average Precson (K = 2) (normalzed) Notce that the precson s consstently better for the learned parameters over the default settngs. Ths generally apples both to the tranng data (as you would expect) and to the testng data as well! One nterestng tem to notce s that n comparng K=1 to K=2, we fnd a hgher percentage of the cross-valdaton precsons to be less than the default for the case of K=2. Ths suggests that the learnng algorthm s overtranng on the tranng data and that the algorthm has too much varance and not enough bas. Lookng for trends n the data, I wll suggest why ths mght be the so. Parameter optmzaton and trends The learnng algorthm parttons the parameters nto 4 groups by ther normalzaton. In ths dscusson, t s convenent to thnk of the parameters n these natural groupngs. Total Score Group Parameters θ 15-17, behave n one of 2 dstnct ways. Ether θ 17 s domnate (.e. has a value between.9 and 1.), or the weght s roughly evenly dstrbuted among the 3 parameters. For the case where θ 17 s domnate, the dfference n test precsons between default and optmzed parameters tend to be larger than when the weght s more evenly dstrbuted. Even though the parameters are multpled, and thus can be treated as a sngle entty, arrangng them n ths fashon allowed the learnng algorthm to

4 retran ths parameter multple tmes. Experment tests show that the learnng algorthm performs less effectvely f the parameters are collapsed nto a sngle entty. TF Group For each tral the optmzaton of both θ and θ 4 was essentally. Effectvely, the algorthm has provded expermental evdence to suggest that one would not want to use an exponental or constant term frequency. The other parameters n ths group θ 1-3 were gven approxmately equal weghts and grouped wth apparent randomness. That s to say, for one tral, θ 1 and θ 2 mght each have 1/2 whle θ 3 s whle on another tral θ 1, θ 2, and θ 3 mght each have 1/3. On other trals, ether θ 1, θ 2 or θ 3 mght be 1 whle the other 2 zero. Because there s not a strong correlaton between any combnaton and a more precse result, the data suggests that any of the scalng functons (lner, logarthmc or root) wll have an equal beneft on the overall query precson. IDF Group The parameter θ 7 domnated ths group. As s evdenced by the low devatons for each element n ths group (gnorng the nosy case of K=5, whch s not enough tranng samples), the values for each theta were essentally the same for each set of tranng data. Whle the other values, θ 5, θ 6, θ 8, θ 9 were each zero, or very near zero, θ 7 was consstently at or very near 1. So, the expermental data suggests that a good classfer wll use log( n /( df + 1)) as a scorng functon for df. 2 COORD Group As evdenced by the low devatons for these parameters, they nearly always optmzed to the same value. In the case of θ 11, θ 12, θ 13, that value was always zero. The values of θ 1, and θ 14 were coupled. Ether θ 14 was one and θ 1 zero, or they both were a half. There s a hgh correlaton (about 66%) of the case where θ 1 θ 14.5 and the test results beng lower for the optmzed parameters than the default parameters. So, t would seem that t s more advantageous (hgher bas) to have θ 14 equal 1 and the others set to zero. Functonally, ths corresponds to only takng nto consderaton the number of matches between a query and a document and gnorng the sze of the document altogether. 2 Recall that n s the number of documents n the corpus whle df s the number of documents whch contan the term of nterest. Benchmarkng Durng the dscusson of the tests, I have shown that the optmzaton process can produce gans n average precson over the top ten results. Now, I wll move the dscusson to nclude some other tests to see f the optmzed query parameters are really better, or f the gans n precson came at too great a cost. Precson - Recall One metrc common n IR s to plot a precsonrecall curve. Comparng both the default and optmzed parameters, the followng precsonrecall curves are produced: Precson Default Recall Learned It s easy to tell from the graph that there s not a notceable dfference between the two classfers when averaged over all the queres. Generally, the learned classfer has a slghtly hgher precson rate as evdenced by the plot below. For the plot, postve values ndcate that the learned classfer performed better,.e. the dfferences as calculated as learned - default. Dfference n Precson Recall Clearly, by measurng only the rate of precson at a gven recall level, there s not a sgnfcant dfference between the learned and default classfer. Whle ths result may not be terrbly mpressve, t does show that the learned query parameters have a smlar bas and varance as the default query parameters. Now, havng shown that the learned parameters are on average no worse than the default parameters, lets take a closer look at precson n the top 1.

5 Snce we were tryng to optmze the precson n the top ten documents, we would expect to see some performance ncreases for the top ten results. The queres can be grouped nto three groupngs: those where the precson was the same, those where the learned performed better and those where the default performed better. Default was better (18 queres) Typcally, the dfference between default and learned was only one, n fact the of the 18 queres n ths group, only one query had a dfference less than -1. Many of the losses were from documents movng from the 9 th or 1 th rank to a slghtly lower rank. Only two queres whch had relevant results n the top ten for the default had none for the learned parameters. Learned was better (42 queres) As was the case when the default was better, the dfference n ths group was typcally only 1. However, there were two cases where the precson was mproved by 2, and 1 case where the precson was mproved by 4. Moreover, there were two cases where the number of relevant documents ncreased from to 1. Precson was the same (166 queres) For most of the queres, the precson n the top ten was the same. The only dfferences n ths group were that the rankng of ndvdual documents was somewhat re-ordered. In rare cases, a relevant document was replaced wth one whose orgnal rank was greater than 1. Learnng Rate As mentoned prevously, the default values represent a stubborn maxmum such that t s hard to explore beyond t. To get around ths feature, I was forced to ntalze the parameters for each theta randomly for each tranng tral. Fortunately, the learnng algorthm typcally converged after only 5 or 6 passes through that data. Below s a chart whch summarzes a learnng curve from one of the examples. Conclusons Usng the optmzatons found by the learnng algorthm, one can expect to see more precse results n the top ten queres about 2% of the tme, whle experencng less precse results about 8% of the tme. Ths net gan ndcates that the learned parameters provde some gan for the top ten results, but as evdenced by the precson-recall fgure, these gans do not accumulate over tme. Beyond smply the gan n precson, there are several other results whch are worth notng. Frst, the algorthm was successful n demonstratng that some of the proposed features are bad canddates and should not be used n an nformaton retreval system. One surprsng result was that the algorthm was unable to consstently select parameters for the TF group. The data suggests that usng lnear, logarthmc or root scalng of the term frequency wll have bascally the same effect. I had expected that the lnear parameter would be optmzed to a very low value. Another surprsng expermental result was that the default values represented a stubborn local maxmum, and f I ntalzed usng these values, the learnng algorthm could not recover from ths maxmum. Acknowledgements: Dr. Chrstopher Mannng (Stanford) was helpful by provdng the Cranfeld data fles as well as suggestng canddate scalng functons to parameterze. Lucene ( was the backbone of the project. The open-source lbrares allowed me to buld custom ndces and run queres aganst them whle tranng. Learnng Curve Iterato ns Classfer Default

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