Patent Abstract:
Systems and methods for determining the topic structure of a document including text utilize a Probabilistic Latent Semantic Analysis (PLSA) model and select segmentation points based on similarity values between pairs of adjacent text blocks. PLSA forms a framework for both text segmentation and topic identification. The use of PLSA provides an improved representation for the sparse information in a text block, such as a sentence or a sequence of sentences. Topic characterization of each text segment is derived from PLSA parameters that relate words to “topics”, latent variables in the PLSA model, and “topics” to text segments. A system executing the method exhibits significant performance improvement. Once determined, the topic structure of a document may be employed for document retrieval and/or document summarization.

Full Description:
BACKGROUND OF THE INVENTION 
     1. Field of Invention 
     This invention relates to segmentation and topic identification of a portion of text, or one or more documents that include text. 
     2. Description of Related Art 
     In long text documents, such as news articles and magazine articles, a document often discusses multiple topics, and there are few, if any, headers. The ability to segment and identify the topics in a document has various applications, such as in performing high-precision retrieval. Different approaches have been taken. For example, methods for determining the topical content of a document based upon lexical content are described in U.S. Pat. Nos. 5,659,766 and 5,687,364 to Saund et al. Also, for example, methods for accessing relevant documents using global word co-occurrence patterns are described in U.S. Pat. No. 5,675,819 to Schuetze. 
     One approach to automated document indexing is Probabilistic Latent Semantic Analysis (PLSA), also called Probabilistic Latent Semantic Indexing (PLSI). This approach is described by Hofmann in “Probabilistic Latent Semantic Indexing”, Proceedings of SIGIR &#39;99, pp. 50–57, August 1999, Berkley, Calif., which is incorporated herein by reference in its entirety. 
     Another technique for subdividing texts into multi-paragraph units representing subtopics is TextTiling. This technique is described in “TextTiling: Segmenting Text into Multi-paragraph Subtopic Passages”, Computational Linguistics, Vol. 23, No. 1, pp. 33–64, 1997, which is incorporated herein by reference in its entirety. 
     A known method for determining a text&#39;s topic structure uses a statistical learning approach. In particular, topics are represented using word clusters and a finite mixture model, called a Stochastic Topic Model (STM), is used to represent a word distribution within a text. In this known method, a text is segmented by detecting significant differences between Stochastic Topic Models and topics are identified using estimations of Stochastic Topic Models. This approach is described in “Topic Analysis Using a Finite Mixture Model”, Li et al., Proceedings of Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 35–44, 2000 and “Topic Analysis Using a Finite Mixture Model”, Li et al., IPSJ SIGNotes Natural Language (NL), 139(009), 2000, each of which is incorporated herein by reference in its entirety. 
     A related work on segmentation is described in “Latent Semantic Analysis for Text Segmentation”, Choi et al, Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, pp 109–117, 2001, which is incorporated herein by reference in its entirety. In their work, Latent Semantic Analysis is used in the computation of inter-sentence similarity and segmentation points are identified using divisive clustering. 
     Another related work on segmentation is described in “Statistical Models for Text Segmentation”, Beeferman et al., Machine Learning, 34, pp. 177–210, 1999, which is incorporated herein by reference in its entirety. In their work, a rich variety of cue phrases are utilized for segmentation of a stream of data from an audio source, which may be transcribed, into topically coherent stories. Their work is a part of the TDT program, a part of the DARPA TIDES program. 
     SUMMARY OF THE INVENTION 
     The systems and methods according to this invention provide improved text segmentation of a document with improved performance. 
     The systems and methods according to this invention separately provide topic identification of a document with improved performance. 
     The systems and methods according to this invention separately determine the topic structure of one or more documents. 
     The systems and methods according to this invention separately provide improved document retrieval. 
     In various exemplary embodiments of the systems and methods according to this invention, the topic structure of a document including text is determined by: identifying candidate segmentation points of the text of the document corresponding to locations between text blocks; applying a folding-in process to each text block to determine a distribution of probabilities over a plurality of latent variables for each text block; using the determined distributions to estimate a distribution of words for each text block; making comparisons of the distributions of words in adjacent text blocks using a similarity metric; and selecting segmentation points from the candidate segmentation points of the text based on the comparison to define a plurality of segments. In various exemplary embodiments, making comparisons of the distributions of words in adjacent text blocks using a similarity metric is based on at least one of a variational distance, a cosine distance, a Hellinger or Bhattacharyya distance, a Jensen-Shannon divergence, a weighted sum and a geometric mean. 
     In various exemplary embodiments of the systems and methods according to this invention, a folding-in process is applied to each segment to determine a distribution of probabilities of latent variables for each segment. Each determined distribution is used to estimate a distribution of words for each segment and at least one topic is identified for each segment based on the distribution of words for each segment. 
     In various exemplary embodiments, identifying at least one topic for each segment is also based on a measure of an occurrence of a word in each segment. In such exemplary embodiments, identifying at least one topic for each segment may be based on the distribution of words for each segment and an inverse segment frequency of the words in the segments. 
     In various exemplary embodiments, identifying at least one topic for each segment is also based on term vectors for each segment. In various other exemplary embodiments, identifying at least one topic for each segment is also based on parts of speech of the words in the segments. In various other exemplary embodiments, identifying at least one topic for each segment is also based on mutual information between words in each segment and each segment. 
     In various exemplary embodiments of the systems and methods according to this invention, a document including text is retrieved by determining topic structures of a plurality of documents and retrieving at least one of the plurality of documents using the topic structures of the documents. In such exemplary embodiments, retrieving at least one of the plurality of documents using the topic structures of the documents is based on at least one key word. 
     These and other features and advantages of this invention are described in, or are apparent from, the following detailed description of various exemplary embodiments of the systems and methods according to this invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various exemplary embodiments of the systems and methods of this invention described in detail below, with reference to the attached drawing figures, in which: 
         FIG. 1  is a graphical representation of word error rate for segmentation based on the number of EM iterations; 
         FIG. 2  is a graphical representation of folding-in likelihood based on the number of EM iterations; 
         FIG. 3  is an outline of an exemplary embodiment of a segmentation method according to this invention; 
         FIG. 4  is a graphical representation of segment similarity based on the exemplary embodiment illustrated in  FIG. 3 ; 
         FIG. 5  is a graphical representation of smoothed and unsmoothed similarity values; 
         FIG. 6  is a block diagram of an exemplary embodiment of a topic identification system according to this invention; 
         FIG. 7  is an exemplary flowchart illustrating a conventional method of preparing training data; 
         FIG. 8  is a flowchart illustrating an exemplary embodiment of a segmentation method using one PLSA model according to this invention; 
         FIG. 9  is a flowchart illustrating an exemplary embodiment of a segmentation method using a plurality of PLSA models according to this invention; and 
         FIG. 10  is a flowchart illustrating an exemplary embodiment of a topic identification method according to this invention. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     In various exemplary embodiments, the systems and methods according to this invention determine the topic structure of a document by segmenting and identifying the topics in the document. The systems and methods according to this invention employ Probabilistic Latent Semantic Analysis (PLSA). The distance between adjacent blocks of text in the document are compared and segmentation points are selected based on similarity values between pairs of adjacent blocks. In various exemplary embodiments, a vector generated by folding a term vector into a Probabilistic Latent Semantic Analysis model is used to exploit information about semantically similar words. 
     The systems and methods according to this invention not only segment a document by topic and/or subtopic, but also identify the various topics/subtopics in the text. The identification of topics/subtopics according to this invention is extensible to a variety of genres, such as news articles, books and scientific papers or any other text. Further, the identification of topics/subtopics according to this invention may be independent of cue phrases. 
     In various exemplary embodiments, the systems and methods according to this invention retrieve a document including text by determining topic structures of a plurality of documents as described herein. Using the topic structures of the documents, at least one of the plurality of documents may be retrieved, for example, based on a key word that is input for searching. 
     The following description of an exemplary embodiment and various alternatives is by way of example only, and is not intended to be exhaustive or limiting. On the contrary, the exemplary embodiment and various alternatives are intended to provide those skilled in the art with a full understanding of this invention. 
     In an exemplary embodiment, a plurality of documents including text for which the topic structure is to be determined are identified or selected. Each document is first preprocessed by: (1) tokenizing the document; (2) downcasing each token; (3) stemming each token; and (4) identifying sentence boundaries. Steps 1–3 identify terms in the vocabulary of the text. A subset of the terms of the vocabulary is selected based on the frequency of the terms in the text. For example, only those words with a frequency above a given threshold are used. 
     The selection of an appropriate subset of the terms enhances performance. The full set of the terms of the vocabulary is noisy and may decrease performance. If too few of the terms of the vocabulary are selected, the subset is sparse, which may lead to an inability to determine a reasonably good estimate of the similarity between blocks of text, as described below. A sparse subset can, in part, be offset by using a larger text block size, but the precision of determining a topic boundary is decreased thereby. 
     The smallest unit for the segmentation process is an elementary block of text. An elementary block of text is a unit before and after which a segment boundary can occur, but within which no segment boundary can occur. For example, sentences may be used as elementary blocks of text, but other (variable or fixed sized) units are also possible, such as, for example, paragraphs. 
     The text of the document being pre-processed is broken into sequences of consecutive elementary blocks referred to as text blocks. Each text block comprises a certain number of elementary blocks. In training documents, text blocks are variable-sized, non-overlapping and generally do not cross segment boundaries. However, in the documents to be segmented, text blocks may be overlapping, as in the use of a sliding window. For the actual segmentation process, we define a text block size h, so that text blocks are composed of h elementary blocks. The set of locations between every pair of adjacent text blocks comprise candidate segmentation points. 
     Each text block b is represented by a term vector f(w|b) representing the frequency of terms or words w in the text block. The text blocks in the entire training collection of documents are used in estimating the parameters of a Probabilistic Latent Semantic Analysis model, described below, using an Expectation-Maximization or EM algorithm, where the number of latent variables or “clusters” Z is preset. The EM algorithm is described in “Maximum Likelihood from Incomplete Data via the EM Algorithm”, Dempster et al., Journal of the Royal Statistical Society, 39(1), pp. 1–21, 1997, which is incorporated herein by reference in its entirety. 
     Based on experimental results, a useful number of clusters is approximately twice the number of human-assigned topics, depending on the nature of the documents. 
     Probabilistic Latent Semantic Analysis (PLSA) utilizes a statistical latent class model or aspect model, as described in the article “Probabilistic Latent Semantic Indexing” by Hofmann, incorporated by reference above. The model is fitted to a training corpus by the EM algorithm. The EM algorithm assigns probability distributions over classes to words and documents. This allows the words and documents to belong to more than one class, and not to only one class as is true of most other classification methods. Probabilistic Latent Semantic Analysis represents the joint probability of a document d and a word w based on a latent class variable z: 
     
       
         
           
             
               
                 
                   
                     
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     A model is fitted to a training corpus D by maximizing the log-likelihood function L using the EM algorithm: 
     
       
         
           
             
               
                 
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     Iterations of the EM algorithm maybe run until the log likelihood does not decrease significantly. However, a small number of iterations, for example, twenty, may be sufficient. The segmentation process is not very sensitive to the exact number of EM iterations performed in the Probabilistic Latent Semantic Analysis training process.  FIG. 1  illustrates the word error rate for segmentation of the Reuters-21578 corpus using 128 classes. As shown in  FIG. 1 , error rate significantly decreases during the first few iterations or steps, and then flattens out. If labeled held-out data is available, one can draw the corresponding graph for the held-out data and decide on the number of iterations. If no labeled held-out data is available, one can use the folding-in likelihood instead.  FIG. 2  illustrates the folding-in likelihood values word error rate for segmentation of the Reuters-21578 corpus using 128 classes. As shown in  FIG. 2 , the folding-in likelihood flattens out after approximately the same number of iterations or steps as the graph for word error rate and can be used to indicate when to stop training iterations. The folding in likelihood for a data set consisting of one or more text documents q may be calculated by: 
                 ℒ   fi     ⁡     (   Q   )       =       ∑     q   ∈   Q               ⁢           ⁢       ∑     w   ∈   q               ⁢           ⁢       f   ⁡     (     w   ⁢             i     ⁢           ⁢   q     )       ⁢   log   ⁢       ∑             ⁢   z               ⁢           ⁢       P   ⁡     (     w   |   z     )       ⁢       P   fi     ⁡     (     z   |   q     )                       
where P(w|z) are the parameters obtained from the PLSA model and P fi (z|q) are determined by folding-in.
 
     Estimation of the parameters yields distributions P(z|b) for the training blocks b and latent variables z, and P(w|z) for the selected words w. The distributions P(w|z) are used in the segmentation process described below. 
     One uses the parameters P(w|z) obtained in the training process to later calculate P(z|q) for the actual documents q with the folding-in process. In the folding-in process, Expectation-Maximization is used in a similar manner to the training process: the Expectation step is identical, the Maximization step keeps all the P(w|z) constant, and only P(z|q) is re-calculated. Typically, a very small number of iterations is sufficient for folding-in. 
     Candidate segmentation points are identified during the pre-processing of the documents. The candidate segmentation points correspond to the locations between the text blocks. Folding-in, as described in article “Probabilistic Latent Semantic Indexing” by Hofmann, incorporated by reference above, is then performed on each text block b to determine the probability distribution among the set of clusters, P(z|b), where z is a latent variable. The estimated distribution of words for each block b, P(w|b), is then determined: 
                     P   ⁡     (     w   |   b     )       =       ∑             ⁢   z               ⁢           ⁢       P   ⁡     (     w   |   z     )       ⁢     P   ⁡     (     z   |   b     )                   (   3   )               
for all words w, where P(w|z) is taken from the Probabilistic Latent Semantic Analysis clustering of the training documents. The distribution of words w in adjacent text blocks is compared using a similarity metric. A “dip” is a local minimum in the similarity of adjacent text blocks. The depth of a dip relative to an adjacent peak is the difference between the similarity value at the peak and the similarity value at the dip, sim(b l , b r ). The size of a dip is the average of the depth of the dip relative to the peak to the left, max l , and the peak to the right, max r . The relative size of a dip is the size of a dip divided by the similarity value at the dip:
 
     
       
         
           
             
               
                 
                   
                     
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     Other approaches can also be used. For example, the maximum of the depth of the dip relative to the left peak and the depth of the dip relative to the right peak may be used. Then, the relative size of a dip is computed as: 
     
       
         
           
             
               
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     If the number of segments is known in advance, the dips with the largest relative dip size are selected as the segmentation points. If the number of segments is not known, a method for automatic termination is needed. In other words, a determination must be made as to which dips actually constitute a segment boundary and which do not. The determination may be made based on a threshold θ term . If the relative dip size d rel  is smaller than this threshold, the dip is ignored. The threshold may be determined in preliminary testing. The threshold may be θ term =1.2, for example. 
     An outline of an exemplary embodiment of a segmentation method according to this invention is shown in  FIG. 3 . 
       FIG. 4  is an exemplary segment similarity graph based on the exemplary segmentation method applied to article #14891 of the Reuters-21578 corpus. As shown in  FIG. 4 , five dips occur in the document at text blocks  6 ,  9 ,  14 ,  19  and  25 . The lowest absolute similarity value is at text block  6 . The relative dip sizes at text blocks  6 ,  14 , and  19  are above a selected threshold of 1.2. Thus, text blocks  6 ,  14  and  19  are selected as segmentation points or boundaries. The relative dip sizes at text blocks  9  and  25  are below the threshold, and are therefore ignored. 
     Smaller dips in similarity values may be indicative of a smaller topic transition or may be due to “noise” in the similarity values when transitioning from one topic to another over several sentences. In most cases, it is desirable to estimate the dip size ignoring the smaller dips. To do this, the similarity values may be smoothed with an n-point median smoother, such as the 3-point median smoother described by Tukey, “Exploratory Data Analysis”, Addison Wesley Longman, Inc., Reading Mass., 1997, which is incorporated herein by reference in its entirety. Other approaches, such as, for example, a weighted sum or geometric mean, can also be used for smoothing the similarity values. Any other suitable smoothing technique, either known or hereafter developed, may be used. The smoothed similarity values may then be used to identify the range of text blocks covered by the dip. 
     The actual dip size should be determined using the smoothed similarity values, since the unsmoothed values tend to reduce the dip size.  FIG. 5  illustrates this situation with the unsmoothed values represented by blocks and the smoothed values represented by T-lines. When determining the relative dip size for the dip at block  7  without smoothing, the surrounding maxima are at blocks  5  and  9 , yielding a relatively small dip. If smoothing is applied to find the maxima and the original values are used to determine the dip size, blocks  3  and  11  are identified as the surrounding maxima, yielding a much deeper dip. 
     Different similarity metrics between two text blocks b l  and b r  may be used in the systems and methods according to this invention. 
     One is based on the variational or L 1  distance: 
     
       
         
           
             
               
                 
                   
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     Another metric is the cosine distance: 
     
       
         
           
             
               
                 
                   
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     A third metric is the Hellinger or Bhattacharyya distance: 
     
       
         
           
             
               
                 
                   
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     A fourth metric is related to the Jensen-Shannon divergence: 
                     sim   JS     =     1   -       KL   (         P   l     ⁢              P   l     +     P   r       2     )       +     KL   (       P   r     ⁢              P   l     +     P   r       2     )             2               (   7   )               
where KL (·∥·) is the Kullback-Leibler divergence (or relative entropy).
 
     For each segment s that is identified during the segmentation process described above, P(z|s) is determined by folding-in. The probability distribution P(z|s) is used to determine the word distribution P(w|s), analogously to the determination of the word distribution for a text block described above: 
                   P   (       w   ⁢        s   )       =       ∑   z     ⁢     P   (     w   ⁢        z   )     ⁢     P   (     z   ⁢        s   )                         (   8   )               
where P(w|z) is taken from the Probabilistic Latent Semantic Analysis clustering of the training documents. Those words with high P(w|s) characterize the topic of a segment: The selection of topic words may be:
         based on P(w|s) alone, selecting a certain number of words with highest probabilities;   based on a mixture of P(w|s) and the term vectors for that segment f(w|s), giving higher weight to words that actually occur in the segment—in the extreme case, selecting only words that actually occur in the document;   based on one of the above in combination with a measure of the occurrence of the term in each segment, for example, the “inverse segment frequency”—similar to the “inverse document frequency” as used in the TFIDF weighting well-known in the text retrieval art—so that terms that occur in only a few segments are given higher weight;   additionally based on the part of speech or syntactic category of the words; and/or   based on mutual information between the words and the segments, ranking words based on how well each characterizes a segment and simultaneously differentiates the segment from other segments—an extension of the method outlined by McCallum et al., “A Comparison Event Model for Naïve Bayes Text Classification”, Proc. AAAI-98 Workshop on Learning for Text Categorization, 1998, which describes a method based on the mutual information between the words and relevant and non-relevant documents, and which is incorporated herein by reference in its entirety, with each segment considered being analogous to the relevant document set and the other segments being analogous to the non-relevant document set so that the words or phrases with the largest value of mutual information for a given segment are selected as representatives characterizing that segment.       
     Sequences of words, such as pairs, triples, etc., may be considered in addition to single words. This requires an adjustment of the PLSA model. Instead of determining probabilities P(w|d) for only single words w, probabilities P(W 1 ,W 2 |d) are determined also for pairs of words, analogously for triples, etc. Pairs generally have a smaller occurrence probability than single words. Therefore, normalization for the length of the sequence is needed. This may be accomplished by taking the nth root and ranking by comparing the values P(sequence|d) 1/n  with n being the length of the sequence. 
     For example, words w for a segment s that are nouns, both common and proper nouns, that actually occur in the segment and that have highest P(w|s) after folding-in may be extracted. When considering single keywords, nouns are more informative than other categories or parts of speech. Thus, keywords may be restricted to nouns, for example, for a document retrieval system. 
       FIG. 6  is a block diagram of an exemplary embodiment of a topic identification system  100  according to this invention. The system  100  may be used to implement, for example, the various flowcharts described below. According to the exemplary embodiment, the system  100  comprises an input device  102 , a data storage device  104 , memory  106  and a display device  110 , which are communicated with each other via a link  101 . 
     In use, a portion of text may be input into the system  100  via the input device  102  and stored in the data storage device  104 . Using memory  106  and accessing the portion of text and one or more PLSA models stored in the data storage device  104 , the processor  108  processes the portion of text according to the method of this invention, for example, applying a folding-in process to determine a probability distribution of a latent variable for a plurality of segments of the portion of text, using each determined distribution to estimate a distribution of words for each segment, and identifying at least one topic for each segment based on the distribution of words for each segment. The identified topic may then be stored in the data storage device and/or output, for example, on the display device  110 . 
     The systems and methods for segmentation and topic identification according to this invention may be implemented on a programmed general purpose computer. However, the systems and methods according to this invention can also be implemented on a special purpose computer, a programmed microprocessor or micro-controller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA or PAL, or the like. In general, any device, capable of implementing a finite state machine that is in turn capable of implementing the outline shown in  FIG. 3  and the flowcharts shown in  FIGS. 7–10  can be used to implement the systems and methods according to this invention. 
     The various blocks shown in  FIG. 6  can be implemented as portions of a suitably programmed general-purpose computer. Alternatively, the various blocks can be implemented as physically distinct hardware circuits within an ASIC, or using a FPGA, a PDL, a PLA or a PAL, or using discrete logic elements or discrete circuit elements. The particular form each of the blocks will take is a design choice and will be obvious and predicable to those skilled in the art. 
     The memory  106  can be implemented using any appropriate combination of alterable, volatile or non-volatile memory or non-alterable, or fixed, memory. The alterable memory, whether volatile or non-volatile, can be implemented using any one or more of static or dynamic RAM, a floppy disk and disk drive, a write-able or rewrite-able optical disk and disk drive, a hard drive, flash memory or the like. Similarly, the non-alterable or fixed memory can be implemented using any one or more of ROM, PROM, EPROM, EEPROM, an optical ROM disk, such as a CD-ROM or DVD-ROM disk, and disk drive or the like. 
     The link  101  can be any known or later developed device or system for connecting the various components of the system  100 . The system  100  may include one or more of a direct cable connection, a connection over a wide area network or a local area network, a connection over an intranet, a connection over the Internet, or a connection over any other distributed processing network or system. In general, the link  101  can be any known or later developed connection or communication system. 
     Further, it should be appreciated that the link  101  can be a wired or wireless link to a network. The network can be a local area network, a wide area network, an intranet, the Internet, or any other distributed processing and storage network. 
     Similarly, the input device  102 , the data storage device  104  and the display device  110  may be any suitable device, either known or hereafter developed, that is capable of carrying out the required function. For example, the input device  102  may be any of a scanner, a keyboard, a CD-ROM, a floppy disk and the like. The data storage device  104  may be any of a hard drive, a floppy disk drive, a CD-ROM drive and the like. The display device  110  may be a monitor, a projector, a printer, a speaker and the like. 
       FIG. 7  is an exemplary flowchart illustrating a conventional method of preparing training data. Control begins in step S 7000  and continues to step S 7010 , where a training collection of text, such as a document d, is provided. Then, in step S 7100 , the training collection is subjected to preprocessing, such as stemming, downcasing and the like, and broken down into a plurality of text blocks b. Next, in step S 7200 , the text blocks are then used to estimate the parameters of a PLSA model, as described above. The PLSA model is then used in step S 7300  to determine the probabilities of P(z|d) and P(w|z) for each document d and a word w based on a latent class variable z. Control ends in step S 7310 . 
       FIG. 8  is a flowchart illustrating an exemplary embodiment of a segmentation method using one PLSA model according to this invention. Control begins in step S 8000  and continues to step S 8010 , where a text such as a test document is provided. Then, in step S 8100 , the test document is subjected to preprocessing and, in step S 8200 , split into a plurality of text blocks b. Next, in step S 8300 , the text blocks are subjected to a folding-in process using the PLSA model. The similarity between adjacent text blocks is then calculated in step S 8400  and, in step  8500 , the calculated similarities are used to generate a block similarity curve, such as that illustrated in  FIG. 4 , for example. 
     Once the similarity curve is generated, local minima can be identified in step S 8600 . The dip-size at each minimum is calculated in step S 8700  and, in step S 8800 , the largest dips, or dips that meet a particular threshold, are determined from the calculated dip-sizes. Then, in step S 8900 , segment boundaries for the text document are determined based on the largest dips. Control ends in step S 8910 . 
       FIG. 9  is a flowchart illustrating an exemplary embodiment of a segmentation method using a plurality of PLSA models according to this invention. The flowchart of  FIG. 9  parallels the flowchart of  FIG. 8 , similar numbers identifying corresponding steps. The difference is that, in step S 9300 , a plurality of PLSA models, obtained with different random initializations of the models prior to training each model or using different numbers of latent variables, are used in a plurality of folding-in processes to determine a corresponding plurality of probabilities. Then, in step S 9400 , the similarity between adjacent text blocks is calculated for each set of probabilities. Accordingly, an additional step S 9410  is needed to combine the plurality of similarity curves. 
       FIG. 10  is a flowchart illustrating an exemplary embodiment of a topic identification method according to this invention. For example, the method can be used to determine key words of a given text q. Control begins in step S 1000  and continues to step S 1010 , where the text q is provided. Then, in step S 1020 , the text q is subjected to preprocessing and split into text blocks. Next, in step S 1030 , the text blocks are subjected to a folding-in process using the PLSA model. In step S 1040 , words with the highest probability P(w|q), and meeting any other suitable criteria such as occurrence in the text, syntactic category, mutual information and the like, are selected. Then, in step S 1050 , keywords for the text q are identified. Control ends in step S 1060 . 
     While this invention has been described in conjunction with various exemplary embodiments, it is to be understood that many alternatives, modifications and variations would be apparent to those skilled in the art. Accordingly, Applicants intend to embrace all such alternatives, modifications and variations that follow in the spirit and scope of this invention.

Technology Classification (CPC): 6