Patent Publication Number: US-7590626-B2

Title: Distributional similarity-based models for query correction

Description:
BACKGROUND 
   Search engines have been developed that allow users to search for documents on a network such as the Internet by submitting a search query consisting of one or more search terms. One obstacle to obtaining the proper search results is that users often misspell the terms in their query. To alleviate this problem, many search engines perform spell checking on the query and provide suggestions to the user for correcting their search query. 
   Some systems that perform this spelling correction rely on a source channel model. The source channel model attempts to find a candidate alternative query that has the maximum probability given the input query. This probability is determined by determining two separate probabilities: the conditional probability of the input query given the candidate; and the probability of the candidate. The probability of the candidate is typically determined using a statistical language model that provides the probability that a search query will contain the sequence of words found in the candidate. The conditional probability of the input query given the candidate is determined using an edit distance between the input query and the candidate query. 
   The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
   SUMMARY 
   A distributional similarity measure between a word of a search query and a term of a candidate word sequences is used to determine an error model probability that describes the probability of the search query given the candidate word sequence. The error model probability is used to determine a probability of the candidate word sequence given the search query. The probability of the candidate word sequence given the search query is used to select a candidate word sequence as a corrected word sequence for the search query. Distributional similarity is also used to build features that are applied in a maximum entropy model to compute the probability of the candidate word sequence given the search query. 
   This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a general flow diagram for identifying query corrections. 
       FIG. 2  is a block diagram of elements used in the methods of  FIG. 1 . 
       FIG. 3  is a flow diagram of a method for identifying N-best candidates. 
       FIG. 4  is a flow diagram for determining distributional similarity using a confusion probability. 
       FIG. 5  is a flow diagram for determining distributional similarity using a cosine measure. 
       FIG. 6  is a flow diagram for using a distributional similarity to select a candidate based on a source channel model. 
       FIG. 7  is a flow diagram for using a distributional similarity to select a candidate based on a maximum entropy model. 
       FIG. 8  is a block diagram of a computing environment. 
   

   DETAILED DESCRIPTION 
   Under the embodiments described below, candidate query corrections for an input query can be improved by determining a distributional similarity between words of the candidate and words of the input query. These distributional similarities are applied to a source channel model or a maximum entropy model to identify the candidate that provides the highest probability. In many embodiments, the correction candidate with the highest probability will include a correction to the spelling of at least one query term. 
     FIG. 1  provides a flow diagram of a general method for identifying a correction candidate from an input query.  FIG. 2  provides a block diagram of elements used in the method of  FIG. 1 . 
   In step  100 , query logs  200  are collected based on searches provided to a search engine  202  by a collection of users  204 . Specifically, when a user submits a query to search engine  202 , the query is added to query logs  200 , if it has not been submitted before, or a count representing the number of times that the query has been submitted is incremented. In some embodiments, query logs  200  are periodically reset so that the latest query logs accurately represent the type of searches that are currently being made. 
   At step  102 , a term frequency calculator  206  parses each query in query logs  200  to identify terms in the queries and the frequencies with which the terms are used in the queries. The frequency of a term is the number of queries that the term has been used in. For example, if the term appears in two queries and one of those queries was submitted five times and the other query was submitted six times, the term frequency would be eleven. Term frequency calculator  206  also determines co-occurrence frequencies. A co-occurrence frequency is the number of times pairs of words appear in the same query. The terms, term frequencies and co-occurrence frequencies are output as terms and frequencies  208 . 
   At step  104 , search engine  202  receives an input query  210 . Input query  210  is provided to a candidate identification unit  212 , which uses the input query to identify N-best correction candidate term sequences  214  at step  106 . 
     FIG. 3  provides a flow diagram of a method of identifying N-best candidates  214 . In step  300  of  FIG. 3 , candidate evaluation  212  selects a term of the input query. At step  302 , candidate identification unit  212  accesses terms and frequencies  208  and an edit distance calculator  216  to identify candidate terms in the query log that are within a threshold edit distance of the input query term. A candidate term may be a single word, no word, or multiple words. 
   Edit distance calculator  216  determines the edit distance between the query term and a candidate term by assigning a distance value to each insertion, deletion, and replacement of individual characters in the input query needed to form the candidate term. Under one embodiment, different distances are associated with different character replacements and different distances may be used for the insertion of characters and the deletion of characters. In some embodiments, the weights applied for replacement of characters are associated with the relative proximity of the two characters on a standard QWERTY keyboard layout. Techniques for computing edit distances are well known in the art. 
   After candidates that are within a threshold edit distance of the input term have been identified, the process continues at step  304  where candidate identification unit  212  determines if there are more input terms in the query. If there are more input terms in the query, the process returns to step  300  to select the next term of the input query and step  302  is repeated for the new term. When there are no more terms in the input query at step  304 , the process continues at step  306  where a language model  226  is used to compute language model probabilities for sequences of candidate terms. Each sequence consists of one candidate term for each query term in the search query in the order set by the search query. Under some embodiments, language model  226  is an n-gram language model, such as a bigram language model. 
   Under some embodiments, the language model probabilities are determined in an exhaustive fashion by determining a language model probability for each combination of candidate terms. Alternatively, a left-to-right search may be performed in which the language model probabilities are built incrementally starting from the candidates for the first term of the input query and ending at the candidates for the last term in the input query. During this search, sequences of candidate terms that produce low language model probabilities are pruned from further consideration, resulting in a more efficient search. 
   At step  308 , the N candidate word sequences that provide the highest language probability are selected as N-best correction candidates  214  for the input query. 
   Returning to  FIG. 1 , after identifying N-best candidates  214 , a distributional similarity is computed between the terms of each candidate and the respective terms of the input query using distributional similarity determination unit  218  at step  108 . Possible distributional similarity metrics include confusion probability, cosine metric and Euclidean distance.  FIG. 4  provides a flow diagram for computing distributional similarity using a confusion probability and  FIG. 5  provides a flow diagram for computing distributional similarity using the cosine metric. 
   In step  400  of  FIG. 4 , the first word of the input query is selected. At step  402 , one of the candidate terms from N-best candidates  214  for the selected input word is selected. At step  404 , distributional similarity determination unit  214  identifies all words that co-occur with the candidate and also co-occur with the input word using information taken from terms and frequencies  208 . At step  406 , one of the co-occurrence words is selected and at step  408 , a set of probabilities is determined. These probabilities include: the probability of the co-occurrence word given the candidate term; the probability of the co-occurrence word given the input query word; the probability of the co-occurrence word; and the probability of the query word. The probability of the co-occurrence word given the candidate is computed as: 
   
     
       
         
           
             
               
                 
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   where p(w o |w c ) is the probability of co-occurrence word w o  given candidate term w c , f(w o ,w c ) is the frequency of co-occurrence of candidate term w c  with co-occurrence word w o , f(w o,j ,w c ) as the frequency of co-occurrence of candidate term w c  with co-occurrence word w o,j , and J is the number of words that co-occur with both the candidate term and the input word. 
   The probability of the co-occurrence word given the input word is computed as: 
   
     
       
         
           
             
               
                 
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   where p(w o |w i ) is the probability of co-occurrence word w o  given input query word w i , f(w o ,w i ) is the frequency of co-occurrence of input word w i  with co-occurrence word w o , f(w o,j , w i ) is the frequency of co-occurrence of input word w i  with co-occurrence word w o,j , and J is the total number of words that co-occur with both the candidate word and the input word. 
   The probability of the co-occurrence word is determined as: 
   
     
       
         
           
             
               
                 
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   where p(w o ) is the probability of the co-occurrence word, f(w o ) is the frequency of word w o , f(w x ) is the frequency of word w x , and X is the total number of words stored in terms and frequencies  208 . 
   Similarly, the probability for the query word is determined as: 
   
     
       
         
           
             
               
                 
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   where w i  is the query word. 
   At step  410 , the process determines if there are more co-occurrence words that have not been processed. If there are more words, the process returns to step  406  and a new co-occurrence word is selected. The probabilities of step  408  are then computed from the new co-occurrence word. Steps  406 ,  408  and  410  are repeated until all of the words that co-occur with both the candidate term and the input query word have been processed. At that point, step  412  is performed to compute the confusion probability of the candidate term given the input word. Under one embodiment, this confusion probability is computed as: 
   
     
       
         
           
             
               
                 
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   where p(w i |w c ) is the confusion probability, w o,j  is the j th  co-occurrence word and J is the total number of co-occurrence words that appear with both the input query word and the candidate term. 
   At step  414 , the process determines if there are more candidate terms for the input word. If there are more candidate words, the next candidate term is selected at step  402  and steps  404 ,  406 ,  408 ,  410 , and  412  are repeated for the new candidate term. When a confusion probability has been determined for each candidate word, the process continues at step  416  where normalized confusion probabilities are determined for each candidate term using: 
   
     
       
         
           
             
               
                 
                   
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   where  p (w i |w c ) is the normalized confusion probability, p(w i |w c ) is the confusion probability calculated in EQ. 5, p(w i |w c,s ) is the confusion probability computed in EQ. 5 for input query word w i  given candidate term w c,s  and S is the total number of candidate terms for the input word. 
   The normalized confusion probability provides a better estimate of the probability since it redistributes the probability mass associated with candidate words that have been pruned during the N-best selection of candidates. Thus, it provides a more accurate probability of the candidate words under consideration. 
   After the normalized confusion probability has been determined, it may optionally be interpolated with an edit distance probability at step  417  to form an interpolated probability. This step helps to avoid a zero probability problem that can occur with the confusion probability when the query term does not appear in the query logs or there are few context words for the query term in the query logs. The linear interpolation is defined as:
 
 p *( w   i   |w   c )=λ   p   ( w   i   |w   c )+(1−λ) p   ed ( w   i   |   c )  EQ. 7
 
   where p*(w i |w c ) is the interpolated probability,  p (w i |w c ) is the normalized confusion probability, λ is a weight and p ed (w i |w c ) is the edit distance probability of candidate term w c  being misspelled as input word w i , which is calculated as: 
   
     
       
         
           
             
               
                 
                   
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   where ED is the edit distance between the candidate term and the input word as determined by edit distance calculator  216 . 
   In EQ. 7, λ can be optimized experimentally by testing different values of λ against a training set and selecting the λ value that produces the highest probability for correctly spelled words given an incorrect input. 
   The interpolated probability of EQ. 7 is formed for each candidate term for the selected input word. 
   At step  418 , the process determines if there are more input words in the input query. If there are more input words, the process returns to step  400  and steps  402  through  416  are repeated for the next input word of the query. When all of the input words have been processed, the method of  FIG. 4  ends at step  420 . 
     FIG. 5  provides a flow diagram of a method of computing distributional similarity using a cosine metric. In step  500  of  FIG. 5 , a word of the input query is selected and at step  502 , a candidate term corresponding to the selected input word is selected. At step  504 , words that co-occur with both the candidate term and the input word are identified from terms and frequencies  208 . In addition, the frequency of co-occurrence between each co-occurrence word and each of the candidate term and the input word is determined from terms and frequencies  208 . 
   At step  506 , two vectors are formed, one for the input word and one for the candidate term. The elements of the vectors are the frequency of co-occurrence with the co-occurrence words identified in step  504 . For example, for an input word of “blu” and a candidate term “blue”, co-occurrence words “ribbon”, “sea” and “sky” may be found. The frequency of co-occurrence of these words with the input word may be: 
   “blu” and “ribbon”: 980 
   “blu” and “sea”: 810 
   “blu” and “sky”: 1,558 
   and the frequency of co-occurrence of these words with the candidate term may be: 
   “blue” and “ribbon”:95,000 
   “blue” and “sea”: 83,000 
   “blue” and “sky”: 150,765 
   The frequency of co-occurrence for each word forms a separate element of the vector so that the vector for “blu” is [980, 810, 1558] and the vector for “blue” is [95000, 83000, 150765]. 
   At step  508 , the cosine of the angle between the two vectors is determined using: 
   
     
       
         
           
             
               
                 
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   cos ∠v i v c  is the cosine of the angel between vector v i  for the input word and vector v c  for the candidate term, v i,n  and v c,n  are the n th  elements of vectors v i  and v c  respectively, and N is the total number of elements in the vectors. 
   At step  510 , the method determines if there are more candidate terms for the current input word. If there are more candidate terms, the process returns to step  502 , and steps  504 ,  506  and  508  are performed to determine a cosine of the angle between the vectors representing a new candidate term and the input word. When all of the candidate terms have been processed at step  510 , the method determines if there are more input words at step  512 . If there are more input words in the query, the method returns to step  500  to select the next input word in the query and steps  502  through  510  are repeated for the new input word. When all of the input words have been processed, the method ends at step  514 . 
   Returning to  FIG. 1 , after the distributional similarities have been computed at step  108 , the distributional similarities and a correction model  224  are used by a candidate selection unit  220  of  FIG. 2  to identify a best candidate  222  at step  110 .  FIG. 6  provides a flow diagram of a method of identifying a best candidate  222  using a correction model  224  that is a source channel model.  FIG. 7  provides a flow diagram of selecting a best candidate  222  using a correction model  224  that is a maximum entropy model. 
   In step  600  of  FIG. 6 , one of the N-best candidate word sequences  214  is selected. At step  602 , an error probability is determined from the distributional similarities  218  between the candidate terms of the selected candidate and the input query. For embodiments where the distributional similarity is the interpolated probability of equation 7 above, the error probability is determined as: 
   
     
       
         
           
             
               
                 
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   where p(s i |s c ) is the probability of the entire input query s i  given the entire string of terms in the candidate word sequence s c , K is the number of words in the input query, and p*(w i,k |w c,k ) is the interpolated probability of the k th  word in the input query given the k th  term in the candidate sequence of words as computed in EQ. 7 above. 
   At step  604 , a language model probability for the candidate sequence of words is determined using language model  226 . Under one embodiment, this language model probability is the product of bigram probabilities computed as: 
   
     
       
         
           
             
               
                 
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   At step  606 , the error probability computed in EQ. 10 is multiplied by the language model probability computed in EQ. 11 to form a posterior probability for the candidate word sequence given the input query as: 
   
     
       
         
           
             
               
                 
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   At step  608 , the process determines if there are more candidate word sequences. If there are more candidates, the process returns to step  600  and selects a new candidate word sequence. Steps  602 ,  604 , and  606  are then repeated for the new candidate word sequence. When all of the candidates have been processed at step  608 , the method continues at step  610 , where the candidate word sequence with the highest probability as computed using EQ. 12 above is selected as the correction candidate  222  of  FIG. 2 . 
     FIG. 7  provides a flow diagram for selecting a candidate  222  using a maximum entropy model as correction model  224 . This model generates a posterior probability of a candidate word sequence given an input query as: 
   
     
       
         
           
             
               
                 
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                 13 
               
             
           
         
       
     
   
   where p(s c |s i ) is the posterior probability of candidate word sequence s c  given input query s i ,f n (w c,k ,w i,k ) is a feature that is formed based on the k th  candidate term and the k th  input query word, N is the total number of features, K is the total number of input query words, C is the total number of candidate word sequences and λ n  is a feature weight. The feature weights can be optimized by maximizing the posterior probability on a training set by selecting the λ that maximize the log probability of the correct query given the incorrect query across the entire training set. Under one embodiment, generalized iterative scaling is used to learn the feature weights. 
   Under one embodiment, the features include:
         a language model feature based on the logarithm language model probability of the candidate word sequence;   edit distance features, based on the edit distance between candidate terms and input query terms;   input frequency features, based on the frequency of input query terms in the query logs;   candidate frequency features, based on the frequency of candidate terms in the query logs;   input lexicon features, based on whether input query terms appear in a lexicon;   candidate lexicon features, based on whether candidate terms appear in the lexicon;   phonetic features, based on the distance between the phonetic description of a query term and the phonetic description of a candidate term;   distributional similarity based input term features, based on a combination of distributional similarity and the frequencies of candidate terms and query terms;   distributional similarity based correction candidate features, based on a combination of distributional similarity, the frequencies of candidate terms and query terms, and whether a candidate term is in a lexicon.       

   At step  700  of  FIG. 7 , a candidate word sequence from the N-best candidates  214  is selected. At step  702 , the language model probability of the candidate is determined using language model  226 . In many embodiments, this involves using a bigram language model probability as shown in EQ. 11 above. This language model probability forms the language model feature. 
   At step  704 , one of the candidate words in the selected candidate sequence is selected along with its corresponding word from the input query. At step  706 , edit distance calculator  216  is used to determine the edit distance between the selected candidate word and the selected input query word. This edit distance is compared against a threshold. If it exceeds the threshold, an edit distance binary feature is set to 1. If it does not exceed the threshold, the edit distance binary feature is set to 0. 
   At step  708 , the frequency of the input term is retrieved from terms and frequencies  208  and is compared to a plurality of thresholds. Each threshold is associated with a separate binary input frequency feature that can have values of 1 or 0. If the frequency is greater than the associated threshold, the feature has a value of 1, and if the frequency does not exceed the threshold, the feature has a value of 0. Under one embodiment, 5 features are set with 5 respective thresholds beginning at frequencies of 10,000 and incrementing by 10,000 to a final frequency of 50,000. 
   At step  710 , the frequency of the candidate word is retrieved from terms and frequencies  208  and is compared to a set of threshold frequencies. Each threshold is associated with a separate binary candidate frequency feature that can have values of 1 or 0. If the frequency of the candidate word exceeds the threshold associated with the feature, the feature is set to 1. If it does not exceed the threshold for the feature, the feature is set to 0. Under one embodiment, 5 thresholds are provided beginning with a frequency of 10,000 and ending at a frequency of 50,000, with intervals of 10,000. 
   At step  712 , candidate selection unit  220  determines if the query term is present in a lexicon  228 . If the query term is present in a lexicon, then a binary input lexicon feature is set to 1. If the input term is not in the lexicon, the input lexicon feature is set to 0. 
   At step  714 , candidate selection unit  220  determines if the candidate term is in lexicon  228 . If the candidate term is in the lexicon, a binary candidate lexicon feature is set to 1. If the candidate term is not in the lexicon, the candidate lexicon feature is set to 0. 
   At step  716 , phonetic descriptions of the candidate term and input word are determined. These can be determined using a text-to-phoneme converter (not shown) or by retrieving the phonetic description from lexicon  228 . The phonetic description of the candidate and input word are compared and the edit distance between their phonetic descriptions is determined. This edit distance represents the difference in the phonetic descriptions between the candidate word and the input word. The phonetic edit distance is then compared to a threshold and a binary phonetic feature is set based on whether the edit distance exceeds the threshold. In particular, the phonetic feature is set to 0 if the phonetic edit distance does not exceed the threshold, and is set to 1 if the phonetic edit distance does exceed the threshold. 
   At step  718  the frequency of the candidate term, the frequency of the input word and the distributional similarity between the candidate term and the input word are used to set a plurality of distributional similarity based input term features. In particular, thresholds are set both for the frequency of the input term and the distributional similarity. Under one particular embodiment, the frequency thresholds are enumerated from 10,000 to 50,000 with intervals of 5,000. The distributional similarity thresholds are numerated from 0.6 to 1 with intervals of 0.1. For each combination of frequency threshold and distributional similarity threshold a separate input term feature is defined. An input term feature will be set to 1 when the frequency of the query term exceeds the threshold associated with the input term feature and the distributional similarity between the candidate word and the input word exceeds the distributional similarity threshold associated with the input term feature. In addition, in order for an input term feature to be set to 1, the candidate term for the input term cannot have a frequency that exceeds the frequency of the input term. 
   At step  720 , candidate selection  220  utilizes terms and frequencies  208 , lexicon  238  and distributional similarities provided by distributional similarity determination unit  218  to set a collection of distributional similarity based correction candidate features. Each correction candidate feature is associated with a separate distributional similarity threshold. Under one embodiment, the distributional similarity thresholds are numerated from 0.6 to 1 with an interval of 0.1. Thus, there are 5 different thresholds and 5 different correction candidate features. A correction candidate feature will be set to 1 when the distributional similarity between the candidate and the input term is higher than the threshold associated with the feature and at the same time the candidate term&#39;s frequency is higher than the query term or the candidate term is in lexicon  228 . 
   At step  722 , candidate selection  220  determines if there are more candidate terms in the selected candidate word sequence. If there are more candidate terms, the process returns to step  704  and steps  704  through  720  are repeated for the new candidate terms. When all of the candidate terms have been processed at step  722 , the method determines if there are more candidate sequences of words at step  724 . If there are more candidate sequences of words, the next candidate sequence is selected by returning to step  700  and step  702  through  722  are repeated for the new candidate sequence of words. 
   When all of the candidate sequences of words have been processed, candidate selection unit  220  computes the posterior probability for each candidate word sequence using equation 13 above. At step  728 , the candidate word sequence that has the highest posterior probability is selected as correction candidate  222  and the process of  FIG. 7  ends. 
     FIG. 8  illustrates an example of a suitable computing system environment  800  on which embodiments may be implemented. The computing system environment  800  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the claimed subject matter. Neither should the computing environment  800  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  800 . 
   Embodiments are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with various embodiments include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, telephony systems, distributed computing environments that include any of the above systems or devices, and the like. 
   Embodiments may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Some embodiments are designed to be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules are located in both local and remote computer storage media including memory storage devices. 
   With reference to  FIG. 8 , an exemplary system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820 , a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . 
   Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
   The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 1  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
   The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 8  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  851  that reads from or writes to a removable, nonvolatile magnetic disk  852 , and an optical disk drive  855  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and magnetic disk drive  851  and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
   The drives and their associated computer storage media discussed above and illustrated in  FIG. 8 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 8 , for example, hard disk drive  841  is illustrated as storing operating system  844 , distributional similarity determination  218 , candidate selection  220 , and program data  847 . 
   A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus. A monitor  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . 
   The computer  810  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG. 8  include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 8  illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
   Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.