Patent Publication Number: US-6990485-B2

Title: System and method for inducing a top-down hierarchical categorizer

Description:
THE FIELD OF THE INVENTION 
     The present invention relates to categorization systems and more particularly to a system and method for inducing a top-down hierarchical categorizer. 
     BACKGROUND OF THE INVENTION 
     Categorization involves assigning items (e.g., documents, products, patients, etc.) into categories based on features of the items (e.g., which words appear in a document), and possibly subject to a degree of confidence. For example: vehicle X that has the features 
                                            number of seats = 55           color = yellow                        
belongs to the category “school buses” with probability 95%.
 
     Hierarchical categorization is the problem of categorizing where the categories are organized in a hierarchy. The field&#39;s terminology has a number of common synonyms, such as: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 categorization = classification, prediction 
               
               
                   
                 features = attributes, properties 
               
               
                   
                 categories = classes, subtopics 
               
               
                   
                 confidence = degree of belief, certainty 
               
               
                   
                 items = cases, examples 
               
               
                   
                 machine learning = supervised learning, induction 
               
               
                   
                   
               
            
           
         
       
     
     In the past, many different systems have been developed for categorizing different types of items. The earliest systems used manual assignment of documents to categories, for example, by human experts. This is currently the dominant method, which is used in libraries, as well as by popular Internet search engine companies. 
     Disadvantages of manual assignment include the fact that it requires a large amount of human resources and it is labor-intensive. In addition, manual assignment is somewhat error-prone and may lead to inconsistencies if people are assigning documents to categories based on different criteria, different interpretations of criteria, or different levels of expertise. 
     To be less subjective, rule-based assignment of documents to categories, including rules based on keywords, has been developed for use with computer systems. This approach uses rules such as “if the document contains the words ‘football’, and ‘goal’, and ‘umpire’ and not the word ‘national’ then assign it to the category ‘local football.’” 
     Mostly, human domain experts author these rules, possibly with the aid of keyword identification tools (such as word counters). These rules usually are comprised of Boolean combinations of keyword occurrences (possibly modified by counts such as “if the term ‘national’ is used at least 5 times then assign to ‘national baseball’”). These rules can be executed automatically, so this solution can be used to automatically assign documents to categories. Examples of human-authored rule classifier systems include a topics search engine by Verity Corp., and email routing software by Kana Communications Inc. 
     The disadvantages of rule-based assignment are that the accuracy of these rules is often very poor. Depending on the authoring of the rules, either the same document is assigned to many categories, including many wrong categories, or to too few categories, in which case documents do not appear in the categories they should. Another disadvantage is that the rules are difficult to author and maintain, and the interaction of the rules (so-called “chaining”) is difficult to understand (and debug), so that unexpected assignments of documents to categories may occur. 
     Categorizers may be built manually by people authoring rules/heuristics, or else built automatically via machine learning, wherein categorizers are induced based on a large training set of items. Each item in the training set is typically labeled with its correct category assignment. The use of predefined categories implies a supervised learning approach to categorization, where already-categorized items are used as training data to build a model for categorizing new items. Appropriate labels can then be assigned automatically by the model to new, unlabeled items depending on which category they fall into. Typically, the larger the training set, the better the categorization accuracy. However, it typically costs something (e.g., human labeling effort) to prepare the training set. 
     Examples of machine learning algorithms include the well-known Naïve Bayes and C4.5 algorithms, support vector machines, and commercial offerings such as those of Autonomy Inc., and Moho Mine Inc. 
     One type of categorizer that can be induced by such machine learning algorithms is a top-down hierarchical categorizer (also referred to as a Pachinko classifier). A top-down hierarchical categorizer typically considers a topic hierarchy one level at a time. At each level, there are typically one or more categorizers that, when assigned a document, pick a category at the next level based on features of the document. 
     A major barrier to using machine-learning categorization technology is that it requires a significant amount of training data, the gathering of which involves significant costs, delays and/or human labor. 
     SUMMARY OF THE INVENTION 
     One form of the present invention provides a method of inducing a top-down hierarchical categorizer. A set of labeled training items is provided. Each labeled training item includes an associated label representing a single category assignment for the training item. A set of unlabeled training items is provided. A prior is associated with the set of unlabeled training items that is independent of any particular feature contained in the unlabeled training items. The prior represents a plurality of possible category assignments for the set of unlabeled training items. A top-down hierarchical categorizer is induced with a machine learning algorithm based on the set of labeled training items, the set of unlabeled training items, and the prior. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a prior art system for inducing a categorizer from a training set of labeled records. 
         FIG. 2  is a block diagram illustrating a prior art system for categorizing unlabeled records with a categorizer that has been induced as shown in  FIG. 1 . 
         FIG. 3  is an electrical block diagram of a computer system configured to implement one embodiment of the present invention. 
         FIG. 4  is a block diagram illustrating a system for inducing a top-down hierarchical categorizer using prior information according to one embodiment of the present invention. 
         FIG. 5  is a hierarchical tree diagram representing a top-down hierarchical categorizer that is induced using prior information according to one embodiment of the present invention. 
         FIG. 6A  is a diagram of a table illustrating an association between training documents and prior identifiers according to one embodiment of the present invention. 
         FIG. 6B  is a diagram of a table illustrating an association between prior identifiers and priors according to one embodiment of the present invention. 
         FIG. 7  is a block diagram illustrating a hierarchical categorization system according to one embodiment of the present invention. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     In the following detailed description of the preferred embodiments, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims. 
       FIG. 1  is a diagram illustrating a prior art system  100  for inducing/training a categorizer  106  from a training set  102  of labeled records. Each record in training set  102  is labeled with its correct category assignment. Inducer  104  receives the training set  102  and constructs the categorizer  106  using a machine learning algorithm. 
       FIG. 2  is a diagram illustrating a prior art system  200  for categorizing unlabeled records  202  with a categorizer  106  that has been induced as shown in  FIG. 1 . System  200  includes records without labels (unlabeled records)  202 , categorizer  106  and categories  204 . Categorizer  106  receives unlabeled records  202  and categorizes the unlabeled records  202  as belonging to one of the categories  204 . 
     One type of categorizer  106  that may be induced by machine learning algorithms is a top-down hierarchical categorizer. As shown in  FIG. 1 , such a categorizer may be induced based on a training set  102  of records, with each record labeled with its correct category assignment. One form of the present invention reduces the need to obtain such labeled training data for a top-down hierarchical categorizer, where certain other “prior” information is more easily available. In this context, a “prior” for a document, according to one embodiment, indicates that a document&#39;s correct categorization falls within a certain set of possible categories, whereas the document&#39;s “label” names a single correct category assignment for the document. As described below, in one embodiment, the training set is augmented with documents for which a prior is known, where labels are unavailable for such documents. 
       FIG. 3  is an electrical block diagram of a computer system  300  configured to implement one embodiment of the present invention. Computer system  300  includes processor  302 , bus  310 , display interface  312 , display  314 , main memory  316 , secondary memory  318 , and input device  320 . 
     Processor  302  is coupled to display interface  312 , main memory  316 , secondary memory  318 , and input device  320  via bus  310 . Display interface  312  is coupled to display  314 . Main memory  316  stores data, and application program instructions for execution by processor  302 . In one embodiment, secondary memory  318  includes a disk drive, CD-ROM drive, and/or other non-volatile storage systems. An input device  320 , such as a keyboard, allows a user to enter data and otherwise interact with system  300 . 
     As shown in  FIG. 3 , an inducer module  306  and a hierarchical categorizer module  308  are stored in main memory  316 . In operation according to one embodiment, processor  302  executes inducer module  306 , which induces top-down hierarchical categorizer  308  based on training items with labels  402 A (shown in  FIG. 4 ) and based on training items with priors  402 B (shown in  FIG. 4 ), as described in further detail below. Processor  302  executes the induced hierarchical categorizer  308  to categorize a set of unlabeled items. 
     It will be understood by a person of ordinary skill in the art that functions performed by system  300  may be implemented in hardware, software, firmware, or any combination thereof. The implementation may be via a microprocessor, programmable logic device, or state machine. Components of embodiments of the present invention may reside in software on one or more computer-readable mediums. The term computer-readable medium as used herein is defined to include any kind of memory, volatile or non-volatile, such as floppy disks, hard disks, CD-ROMs, flash memory, read-only memory (ROM), and random access memory. 
     In one embodiment, items to be categorized by top-down hierarchical categorizer  308  comprise documents, such as electronic documents accessible on the Internet. However, it will be understood that further embodiments of the invention are applicable to other types of items and other network architectures (e.g., client/server, local, intermediate or wide area networks), dedicated database environments, or other configurations. 
       FIG. 4  is a block diagram illustrating a system  400  for inducing a top-down hierarchical categorizer  308  using prior information according to one embodiment of the present invention. System  400  includes hierarchy of categories  408 , inducer  306 , top-down hierarchical categorizer  308 , induction algorithm  416 , training items  402 , featurizer  404 , training feature vectors  406 , training set generator  410 , training set  412 , labels  403 , and prior information  413 . 
     Training items  402  include training items with labels  402 A and training items with priors  402 B. In one embodiment, training items  402  are simply raw documents that do not include associated labels or priors, and labels and priors are associated with the documents after the documents have been featurized. In one embodiment, featurizer  404  produces a feature vector for each training item in training items  402 . The set of feature vectors generated for training items  402  is referred to as training feature vectors  406 . 
     The training feature vectors  406  are provided to training set generator  410 . Training set generator  410  also receives hierarchy of categories  408 , labels  403 , and prior information  413 . Based on these inputs, training set generator  410  generates a training set  412  that includes a plurality of feature vectors, with each feature vector having an associated label  403  or prior information  413 . 
     According to one embodiment of the present invention, the hierarchy of categories  408  is provided by a user. The hierarchy  408  represents a “tree” of categories that has “branches” that end at “leaves”. The leaves are the places in the hierarchy where there are no further subdivisions under a given category. 
     In one embodiment, labels  403  are implemented as a table that specifies category assignments for training items  402 A (e.g., document 1023812 is assigned to category A 12 , document 1023813 is assigned to category B 11 , etc.), and each training item in training items  402 A has a single category from the hierarchy of categories  408  associated with the item (i.e., the item&#39;s “label”  403 ). 
     In one embodiment, prior information  413  is implemented as one or more tables that specify for each training item in training items  402 B a set of possible categories from the hierarchy of categories  408  for the item (e.g., document 1023814 is assigned to one of categories A 11  or A 12 , document 1023815 is assigned to one of categories A 11 , A 21 , or A 22 , etc.). One embodiment of prior information  413  is illustrated in  FIGS. 6A and 6B  and is described in further detail below with reference to those Figures. 
     In one form of the invention, inducer  306  repeatedly calls induction algorithm  416  and training set generator  410  as represented by the dotted arrows in  FIG. 4 . In one embodiment, for each call, training set generator  410  generates an appropriate training set  412  based on training feature vectors  406 , labels  403 , prior information  413 , and hierarchy of categories  408 . In one form of the invention, for each call, the training set  412  generated by training set generator  410  includes labels  403  and prior information  413  that are appropriately mapped to essentially provide an all-labeled training set for the place in the hierarchy  408  currently under consideration, as described in further detail below. The generated training sets  412  are used by induction algorithm  416  to induce categorizers for hierarchical categorizer  308 . In one embodiment, induction algorithm  416  may be any general-purpose induction algorithm, such as Naïve Bayes, Support Vector Machines, k-Nearest Neighbors, Neural Networks, or C4.5. 
     In one embodiment, system  400  is implemented as one or more software modules executable by computer system  300  (shown in  FIG. 3 ), and appropriate input information (e.g., training items  402 , hierarchy of categories  408 , labels  403 , and prior information  413 ) is provided to computer system  300  via input device  320 , secondary memory  318 , or via some other input mechanism. 
       FIG. 5  is a hierarchical tree diagram representing a hierarchical categorizer  308  according to one embodiment of the present invention. Hierarchical categorizer  308  includes a top or root categorizer  500 , and seven sub-categorizers  502 A,  502 B,  502 A- 1 ,  502 A- 2 ,  502 A- 3 ,  502 B- 1 , and  502 B- 2  (collectively referred to as sub-categorizers  502 ) under the top categorizer  500 . Sub-categorizers  502 A,  502 B,  502 A- 1 ,  502 A- 2 ,  502 A- 3 ,  502 B- 1 , and  502 B- 2 , include categories  504 A,  504 B,  504 A- 1 ,  504 A- 2 ,  504 A- 3 ,  504 B- 1 , and  504 B- 2 , respectively. Hierarchical categorizer  308  may be considered a “tree” having a “root” at the top categorizer  500  and branches to the various sub-categorizers  502 . At the bottom of hierarchical categorizer  308  are “leaves,” which include categories  504 A- 11 ,  504 A- 12 ,  504 A- 21 ,  504 A- 22 ,  504 B- 11 ,  504 B- 12 ,  504 B- 21 , and  504 B- 22 . Categories  504 A,  504 B,  504 A- 1 ,  504 A- 2 ,  504 A- 3 ,  504 B- 1 ,  504 B- 2 ,  504 A- 11 ,  504 A- 12 ,  504 A- 21 ,  504 A- 22 ,  504 B- 11 ,  504 B- 12 ,  504 B- 21 , and  504 B- 22 , are collectively referred to herein as categories  504 . The categories  504  correspond to the hierarchy of categories  408  (shown in  FIG. 4 ). 
     During categorization according to one embodiment, hierarchical categorizer  308  works top-down. Top categorizer  500  chooses which of the two branch paths below it to follow, and each sub-categorizer  502  selects which local branch path below that sub-categorizer  502  to follow. Further sub-categorizers  502  would be added for more complex categorization systems. Each sub-categorizer  502  represents the beginning of a “subtree.” The hierarchical categorizer  308  shown in  FIG. 5  is provided as one example for purposes of simplifying the present disclosure and is not intended to limit the invention to the specific illustration of the categorizer  308 . 
     In one embodiment, when training each sub-categorizer  502 , all of the training items with labels  402 A that fall within the subtree of that sub-categorizer  502  have their labels temporarily mapped to the (smaller) set of branch choices directly below that sub-categorizer  502 . For example, during training of sub-categorizer  502 A, training items  402 A having labels corresponding to category  504 A- 11  or  504 A- 12  would be temporarily mapped to category  504 A- 1 , and would be training examples for category  504 A- 1 . Similarly, training items  402 A having labels corresponding to category  504 A- 21  or  504 A- 22  would be temporarily mapped to category  504 A- 2 . The induction algorithm  416  looks at the features of each training item and the category that the training item belongs in, and essentially determines what the pattern of features is that puts the training item in that category. 
     In one form of the invention, a “prior” for a document indicates that the document&#39;s correct categorization falls somewhere within a certain set of categories, whereas the document&#39;s label names a single correct category assignment for the document. 
     For example, assume that books are being categorized into dozens of categories. Assume that category  504 A in  FIG. 5  is “fiction” and category  504 B is “non-fiction,” and that the categories  504  below category  504 A specify fiction topics, and the categories below category  504 B specify non-fiction topics. If it is known that books from a particular technical publisher (e.g., Publisher X) are non-fiction (i.e., we have a prior that books from Publisher X belong in any of the non-fiction topics, but not the fiction topics), all of these books can be used to augment the training set, even if they do not have associated labels for their specific categories. Thus, when training the top categorizer  500  to choose between categories  504 A and  504 B, all of the books from Publisher X can be used as training examples for category  504 B, even if a label identifying a specific category has never been assigned to these books. Therefore, the number of training examples for the top categorizer  500  has been increased, with relatively small added cost (i.e., a human does not have to look at each book and assign a label). The documents under this particular prior are essentially as good as labeled documents when training the root categorizer  500 , but in one embodiment, are not used in further categorization at lower levels in the hierarchy. In this embodiment, for further categorization (e.g., choosing between category  504 B- 1  or  504 B- 2 ), manually labeled training cases would be used as well as any unlabeled cases with priors that fall within these two subtrees. Likewise, if publisher Y produces only books that fall under category  504 A- 1  (e.g., technical manuals), these cases can be used in training both the root categorizer  500  and the categorizer  502 A. More generally, items with priors contained under a given node can be used in training all categorizers that are ancestors of that node. 
       FIGS. 6A and 6B  illustrate one embodiment of prior information  413  (shown in  FIG. 4 ).  FIG. 6A  is a diagram of a table  600  illustrating an association between training documents and prior identifiers according to one embodiment of the present invention. Table  600  includes columns  602  and  604 , and a plurality of entries  606 . Column  602  includes a plurality of document ID numbers for documents in the training set, and column  604  includes a plurality of prior identifiers. Each entry  606  provides an association between a document ID number (in column  602 ) and a prior identifier (in column  604 ). In one embodiment, each document to be used for training is assigned a document ID number, and the number is entered in table  600 . If a document to be used for training includes a known prior, a prior identifier  604  for that prior is entered in the table  600  for that document. In one embodiment, not all documents to be used for training will have a known prior, but they should then have labels to be useful for training. For example, the documents represented by document ID numbers 10,003 and 10,004 do not include an associated prior identifier  604  in table  600 . 
       FIG. 6B  is a diagram of a table  610  illustrating an association between prior identifiers and priors according to one embodiment of the present invention. Table  610  includes columns  604  and  614 , and a plurality of entries  616 . Column  604  includes a plurality of prior identifiers, and column  614  includes a plurality of priors. Each entry  616  provides an association between a prior identifier (in column  604 ) and a prior (in column  614 ). 
     In one embodiment, prior information in tables  600  and  610  is accessed and used during training of hierarchical categorizer  308 . Table  600  is accessed to determine if a document (represented by its document ID number  602 ) includes an associated prior identifier  604 . If the document does have an associated prior identifier  604 , table  610  is accessed to identify the prior  614  associated with the prior identifier  604 . The identified prior  614  indicates possible categories  504  that the document under consideration might fall under. For example, the first prior  614  listed in table  610  is “A 11  or A 22 ” which represents category  504 A- 11  or  504 A- 22  in  FIG. 5 . Thus, any document identified in table  600  that has a prior identifier  604  of “1” will fall under either category  504 A- 11  or  504 A- 22 . Similarly, documents with a prior identifier  604  of “2” will fall under either category  504 A- 1  or  504 A- 3 , documents with a prior identifier  604  of“3” will fall under either category  504 B- 1  or  504 B- 21 , and documents with a prior identifier  604  of “4” will fall under one of categories  504 B- 1 ,  504 A- 11 , or  504 A- 12 . In an alternate embodiment, each prior  614  explicitly lists every single possible category for the item (e.g., instead of just listing “A 1 ”, the prior would list “A 1  or A 11  or A 12 ”). This more powerful representation allows for specifying that a set of items may fall anywhere under A except A 11 . 
     In one embodiment, prior information is manually entered and associated with training documents as the documents are entered into the system for training. This typically involves far less effort than assigning a specific label to each case. For example, if a set of 15,000 documents are received from Company X, and it is known that documents from Company X all have a prior identifier  604  of “1,” then a human operator can easily associate this prior information with all 15,000 documents at one time. In contrast, to go through each one of the 15,000 documents individually and assign a specific label would take a much larger amount of time and human resources. 
     For the purposes of obtaining the most leverage from the priors  614 , in one embodiment, the hierarchy  408  is preferably organized in such a way that most training items&#39; priors  614  have all of their possible categories  504  falling within a single subtree of the hierarchy  408 , as small as possible. For example, if many training items  402 B come with a prior  614  that specifies various non-fiction categories  504 , the non-fiction categories  504  would preferably be gathered into one small subtree (e.g., the subtree beginning at category  504 B- 2 ), rather than having these categories  504  spread randomly throughout the hierarchy  408 . 
     Priors  614  are as valuable as labels whenever all of their permissible categories  504  map to the same branch choice. For example, in the tree shown in  FIG. 5 , if a frequent prior  614  specifies categories “ 504 A- 2 ,  504 A- 21  or  504 A- 22 ,” which all map to the middle branch choice (i.e., category  504 A- 2 ) of sub-categorizer  502 A, in one form of the invention, the sub-categorizer  502 A uses these items as though they were labeled training cases for the middle branch. When inducing sub-categorizers  502  that consider each branch separately against its siblings jointly (e.g., Naïve Bayes), a prior that includes two branches (e.g., categories  504 A- 1  or  504 A- 3 ) is used in one form of the invention as a labeled training example (a negative example) when training the sub-categorizer  502 A for the third branch (i.e., category  504 A- 2 ). 
     In one embodiment, the hierarchy of categories  408  may be restructured automatically or by hand for the purpose of yielding greater leverage of the priors  614 . For example, if a prior  614  for a very large number of training items  402 B specifies “A 12  or A 22 ”, then the hierarchy  408  might be profitably permuted by placing these two nodes as siblings under a replacement node A 1 ′ and placing A 11  and A 21  into a replacement node A 2 ′. Furthermore, portions of the hierarchy  408  might be flattened to leverage a prior  614 . In this example, A 1  and A 2  might be eliminated to make A 11 , A 12 , A 21 , and A 22  siblings of A 3  directly under A. Items within this prior  614  may then be used as negative training cases for A 11 , A 21  and A 3 . In the limit, this strategy may also be used to completely flatten the hierarchy  408  to a single root node containing all the categories as direct children. 
     In one embodiment, priors  614  are phrased as constraints on categories, and are used as positive or as negative training examples. For example, if a training item  402 B has prior information that indicates that it is in  504 A- 12  or  504 A- 22 , in one embodiment, the training item is used in the sub-categorizer  502 A as a training example of “not  504 A- 3 ”. 
     In one embodiment, priors are phrased as probabilistic constraints or information. For example, it can be specified that there is an 80% chance that item X belongs in category  504 A- 1  and a 20% chance that it is possibly in category  504 A- 3 . Depending on the application and the desired set-up that is chosen, the probabilities can be required to sum up to one, indicating mutually exclusive and exhaustive categories, or more flexible category information can be used. 
     In one form of the invention, probabilistic information is incorporated by assigning a weight to each training item  402  that is proportional to the probability of the training item  402  being assigned to a particular category  504 . For instance, a “real” labeled training item  402 A for category  504 A- 1  gets weight 1, whereas an example that has an 80% chance of being in  504 A- 1  gets counted with weight 0.8. 
     By using probabilistic information, in one embodiment, prior information may also be used even if the categories  504  to which a training item belongs (according to the prior information) extend beyond one sub-tree. In one form of the invention, this is done by applying a threshold (e.g., if the probability “mass” exceeds 75% then count it as an example) or by using a weight as described above, or using some similar method such as using a so-called lambda vector (i.e., the possible categories for a prior are represented by fuzzy set membership information). For example, suppose a prior  614  gives a 45% probability mass to category  504 A- 11 , 45% to category  504 A- 12 , and 10% to category  504 B- 11 . In one form of the invention, training items under this prior  614  are used as positive examples for category  504 A- 1  when training the sub-categorizer  502 A, either because 90% is deemed to be “good enough” (exceeds a specified threshold), or a weight of 0.9 exceeds a specified threshold, or some similar threshold is satisfied. 
     In one embodiment, probabilistic information is obtained by running a large number of training items  402 B with a particular prior  614  through an existing categorizer and observing the probability distribution. For example, assume that a collection of documents is received from vendor X for a document hierarchy. Using a categorizer, a distribution can be derived as to where the items in this collection belong (i.e., 32% of items from vendor X are categorized in category  504 A- 11 ). A new categorizer is then trained using this additional prior information. 
     The following is a pseudo-code procedure (“Train”) for implementing one embodiment of inducer  306  and training set generator  410 , for inducing a top-down hierarchical categorizer  308  according to one form of the present invention: 
     
       
         
           
               
             
               
                   
               
               
                 Pseudo Code Example 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
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                 procedure Train(H,T,P,A): HC 
               
               
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                 Let S={S 1 , . . . ,S N } be the direct children of the root of H. 
               
               
                 4 
               
               
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                 Let T2 be a copy of T, where the labels have been restricted to 
               
               
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                 the set S. 
               
               
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                 Add to T2 any training case in P where its set of allowable 
               
               
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                 labels falls entirely under a single node S J . 
               
               
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                 Trivial Base Case 
               
               
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                 IF H contains only a single category, 
               
               
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                 THEN return a categorizer that always returns this single 
               
               
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                 category, no matter what its input feature vector. 
               
               
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                 (ELSE continue this procedure) 
               
               
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                 Induction Case 
               
               
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                 Apply the induction algorithm A to the items in the training 
               
               
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                 set T2 to produce a new categorizer C. 
               
               
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                 Recursion 
               
               
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                 for each subtree s in S { 
               
               
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                 Prepare H s &#39;, T s &#39;, and P s &#39; which are each restricted to the 
               
               
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                 subtree s. 
               
               
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                 Recursively call Train(H s &#39;, T s &#39;, R s &#39;, A) and store the 
               
               
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                 result C s  for use below 
               
               
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                 } 
               
               
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                 return a hierarchical categorizer (HC) that 
               
               
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                 (1) applies the categorizer C and determines which subtree 
               
               
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                 s is most appropriate; and 
               
               
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                 (2) applies the categorizer C s  for the chosen subtree and 
               
               
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                 returns its result. 
               
               
                   
               
            
           
         
       
     
     There are four inputs to the procedure Train: (1) H—a hierarchy of categories  408 ; (2) T—a training set (i.e., a set of feature vectors  406  that are labeled with their correct category assignment  504 ); (3) P—a prior training set (i.e., like the training set T, except the label information does not pinpoint a single category  504 , but instead a subset of categories  504  where the feature vector  406  may belong); and (4) A—a general-purpose induction algorithm  416  (e.g., C4.5, Naïve Bayes, etc.) that, given an input training set  412 , outputs a categorizer function C. 
     In one embodiment, parameter A is actually a pointer to a function that implements the general-purpose induction algorithm  416 . In another embodiment, the algorithm A is not a parameter, but is hard-coded into the training routine. The procedure Train outputs HC, which is a trained hierarchical categorizer  308 . 
     The procedure Train is recursive (i.e., the procedure calls itself, but typically with different parameters for each call). The initial call to the procedure Train passes input variables H, T, and P that contain information that is relevant for the whole hierarchy of categories  408  (i.e., H represents the entire hierarchy  408 , and T and P represent the entire training set  412 ). Subsequently, in one form of the invention, when the procedure Train calls itself, the input variables H, T and P contain only the information that is pertinent to the subtree of the hierarchy  408  that the procedure is dealing with at that moment, as described in further detail below. 
     At line  3  of the procedure Train, the statement “Let S={S 1 , . . . , S N } be the direct children of the root of H” indicates that the set of top-level choices in the hierarchy  408  are assigned to S 1 , S 2 , . . . , S N . For the example hierarchy shown in  FIG. 5 , there are two children of the root  500 , so N=2, and S 1  represents category  504 A, and S 2  represents category  504 B. 
     At lines  5 – 6  of the procedure Train, the statement “Let T 2  be a copy of T, where the labels have been restricted to the set S” indicates that T 2  represents a transformed version of the training set  412 , where all of the labels for categories  504  below the set S (i.e., below categories  504 A and  504 B) are re-mapped to labels for their ancestor categories  504  within the set S. For example, if an item were labeled S 2/1/2 , which corresponds to category  504 B- 12  in the embodiment of  FIG. 5  (i.e., S 2  corresponds to category  504 B, S 2/1  corresponds to the first category under S 2  or category  504 B- 1 , and S 2/1/2  corresponds to the second category under S 2/1  or category  504 B- 12 ), its transformed label would be its ancestor in the set S (S 2  in this example), which corresponds to category  504 B. Thus, using the embodiment illustrated in  FIG. 5 , the training set T 2  includes two possible labels—a first label for category  504 A and a second label for category  504 B. 
     At lines  8 – 9  of the procedure Train is the statement “Add to T 2  any training case in P where its set of allowable labels falls entirely under a single node S j .” For the first call to the procedure Train, the set S includes S 1  (i.e., category  504 A) and S 2  (i.e., category  504 B). Thus, any training case in P that has a set of allowable labels that falls entirely under either the subtree at category  504 A or the subtree at category  504 B is added to T 2 . Such cases are as good as labeled training cases for S 1  and S 2 . In one embodiment, items whose set of allowable labels falls under multiple nodes S j  are excluded from T 2 . In this embodiment, if a training case in P has a set of allowable labels that falls somewhere under both categories  504 A and  504 B, the training case is excluded from T 2 . For example, if a prior  614  indicates that the training item could be in category  504 A- 1 , category  504 B- 11 , or category  504 B- 12 , that training item is not useful in deciding between category  504 A or  504 B and is excluded. 
     Lines  11 – 15  of the procedure Train handle a trivial base case. As indicated therein, if the hierarchy of categories, H, contains a single category  504 , then the procedure returns a trivial categorizer that always returns this single category  504 , no matter what its input feature vector. If H contains more than one category  504 , then the procedure continues. 
     Lines  17 – 19  of the procedure Train handle an induction case. As indicated therein, the induction algorithm A is applied to the items in the training set T 2  to produce a new categorizer C. If all the items in T 2  fall under a single branch, then the induction algorithm A need not be run and C is the trivial classifier that selects the single populated branch. The new categorizer will be used at categorization time to select the next branch in the top-down decision process. For the example illustrated in  FIG. 5 , the new categorizer C will be used as the root categorizer  500  to select between categories  504 A and  504 B. 
     Lines  21 – 26  of the procedure Train specify a recursion process. For each subtree s in S, the procedure prepares H s ′, T s ′, and P s ′ which are each restricted to the subtree s, recursively calls itself with the inputs H s ′, T s ′, R s ′, and A, and stores a resulting sub-categorizer, C s . In one embodiment, any training item in P that has a set of allowable labels that falls into multiple subtrees s is excluded from P′. For the embodiment illustrated in  FIG. 5 , for each of the two subtrees S 1  and S 2  (i.e., the subtrees at categories  504 A and  504 B) the procedure Train would be called to induce the sub-categorizers  502  at those subtrees (i.e., sub-categorizers  502 A and  502 B). 
     At lines  29 – 33  of the procedure Train, a hierarchical categorizer (HC)  308  is returned. In one embodiment, the hierarchical categorizer  308  does the following: (1) applies the categorizer C and determines which subtree s is most appropriate; and (2) applies the categorizer C s  for the chosen subtree and returns its result. 
     In one embodiment, additional recursive calls to the procedure Train are made to induce further sub-categorizers  502  for additional levels in the hierarchy (e.g., sub-categorizers  502 A- 1 ,  502 A- 2 ,  502 A- 3 ,  502 B- 1 , and  502 B- 2 ). 
     The above pseudo code example for the procedure Train is provided to illustrate one embodiment of inducer  306  and training set generator  410 . It will be readily apparent to persons of ordinary skill in the art that various modifications and additions may be made for alternative embodiments. As one example, rather than passing a parameter A (i.e., a pointer to the general purpose induction algorithm  416 ) to the procedure Train, in an alternative embodiment, the code for the induction algorithm  416  is included in a subroutine. 
     After top-down hierarchical categorizer  308  has been trained, it is ready to classify new, unlabeled items.  FIG. 7  is a block diagram illustrating a hierarchical categorization system  700  according to one embodiment of the present invention. System  700  includes unclassified item  702 , featurizer  704 , list of features  706 , hierarchical categorizer  308 , and category  504 . 
     The categorization process starts with an unclassified item  702  that is to be categorized, such as a raw document. The unclassified item  702  is provided to featurizer  704 . Featurizer  704  extracts features from the unclassified item  702 , such as whether a word  1  was present and a word  2  was absent, or the word  1  occurred five times and the word  2  did not occur at all. The features from the featurizer  704  are used to create a list of features  706 . The list of features  706  is provided to hierarchical categorizer  308 , which selects the best category  504  for item  702  based on the provided list of features  706 . 
     During training of hierarchical categorizer  308  according to one embodiment, a set of features is associated with each of the categories  504 . The term “level of goodness” is used herein to describe how good the fit is between the list of features  706  of a document  702  to be categorized and the previously determined features of a category  504 . There are many different ways of determining level of goodness, such as Naïve Bayes, C4.5, Bayesian networks, rule-based multi-category categorizers that output some level of goodness, conditional probability statements, or simple heuristics, among others, or a combination of the foregoing. 
     In one embodiment, root categorizer  500  of hierarchical categorizer  308  computes for item  702  a level of goodness of the match between the item  702  and the categories  504 A and  504 B, and then applies a decision criterion for determining whether the level of goodness is high enough to assign the item  702  to one of these categories  504 A or  504 B. Similarly, each sub-categorizer  502  that is assigned the item  702  computes a level of goodness of the match between the item  702  and the categories  504  under that sub-categorizer  502 , and applies a decision criterion for determining whether the degree of goodness is high enough to assign the document to one of these categories  504 . This process is repeated deeper into the hierarchy until a final, correct category  504  is identified. 
     Priors have been used before in a couple of different categorization techniques. In a first technique, the overall categorization problem has been broken up into sub-problems based on a prior, and then machine learning has separately been applied to each sub-problem. The machine learning algorithm itself does not use the prior information, but rather relies on labeled training items for each of the sub-problems. This technique effectively makes a composite categorizer that depends foremost on the prior attribute. 
     As a potential example of this technique, consider the many Internet directories, such as Yahoo (http://www.yahoo.com) and Infoseek (http://www.infoseek.com) that are largely manually organized in preset hierarchies. For shopping web sites, such as Yahoo shopping, there can be thousands or hundreds of thousands of different products from thousands of different stores. For such sites, rather than manually placing each product into a category within the hierarchy, people typically write textual queries to sort out the various products and identify categories for each product. To further simplify the categorization process, the web site may require companies to identify a top-level category that the company&#39;s products fall under. For example, for the hierarchy shown in  FIG. 5 , the web site may require companies to identify whether its products fall under category  504 A or  504 B (e.g., electronics or gardening supplies). In this way, the categorization problem is broken up into two separate problems. Machine learning (or human authored rules) could be used for one tree (under category  504 A) and could also be used for the second tree (under category  504 B), but would not be used at the root  500 . The information provided by each company is a “prior” as it identifies a set of categories that the product falls under, but it does not identify exactly which category the product falls under. 
     This first technique has several disadvantages. One disadvantage is that the technique depends on the priors being disjoint, which is not always the case. For example, some book publishers produce only non-fiction, and other publishers produce only non-fiction technical material (a smaller set of topics under the non-fiction topic). Thus, priors having different granularities may be provided, but the more specific prior information is not leveraged in the first technique. The additional information that the non-fiction material produced by certain publishers is always technical material is not used in the first technique. 
     A second disadvantage of the first technique is that there is often some non-zero cost associated with obtaining a prior, which one is typically willing to pay for training, but is not typically willing to pay for every future use of the trained categorizer. In other words, using the first technique, even after the categorizer has been trained, priors must be obtained for each and every item to be categorized, so that it can be assigned to the appropriate branch in the hierarchy for further categorization. Obtaining these priors has an associated cost. 
     A third disadvantage of the first technique is that priors may not be available on many incoming cases to be categorized. 
     A second categorization technique that uses priors treats a prior as a predictive feature for every case. Thus, for each training case, the prior is included as an additional feature. This technique carries the second and the third disadvantages described above. Regarding the second disadvantage, since the prior is used as a predictive feature, it is important to have the prior at categorization time. And it may be costly to continually have to obtain this prior information for every case that needs to be categorized in the future. Regarding the third disadvantage, if the prior is not available at categorization time, the prior could be treated as a missing value. However, the induced categorizer might have learned to depend heavily on that attribute, and accuracy will suffer if the missing attribute is treated with standard missing-value techniques (e.g., filling it in with the majority value or most likely value given the other attributes). 
     Embodiments of the present invention do not have the disadvantages associated with the above-described previous techniques for using prior information. In one embodiment, prior information need not be supplied for every training case. One embodiment of the present invention uses priors essentially as labels during training, and then the prior information is not needed during later categorization of unlabeled documents. In one embodiment, priors are used during training of a hierarchical top-down categorizer to increase the amount of training information for the categorizer, and thereby increase the categorization accuracy of the categorizer with relatively minor added cost. In many situations, various priors are freely available, but labels are costlier to obtain. In one embodiment, the use of prior information in training a top-down hierarchical categorization system reduces the amount of labeled training data that is needed for the system. In one embodiment, if the priors are available during classification (not just at training), they may be used to restrict the eligible categories for classification, eliminating some erroneous categories from consideration. 
     Although specific embodiments have been illustrated and described herein for purposes of description of the preferred embodiment, it will be appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent implementations may be substituted for the specific embodiments shown and described without departing from the scope of the present invention. Those with skill in the chemical, mechanical, electro-mechanical, electrical, and computer arts will readily appreciate that the present invention may be implemented in a very wide variety of embodiments. This application is intended to cover any adaptations or variations of the preferred embodiments discussed herein. Therefore, it is manifestly intended that this invention be limited only by the claims and the equivalents thereof.