Patent Application: US-36238009-A

Abstract:
a computer - implemented method , system , and computer program product for generating vector - based similarity scores in text document comparisons considering confounding effects of document length . vector - based methods for comparing the semantic similarity between texts have a characteristic which may reduce their usefulness for some applications : the similarity estimates they produce are strongly correlated with the lengths of the texts compared . the statistical basis for this confound is described , and suggests the application of a pivoted normalization method from information retrieval to correct for the effect of document length . in two text categorization experiments , random indexing similarity scores using pivoted normalization are shown to perform significantly better than standard vector - based similarity estimation methods .

Description:
none of the previously noted work has taken the further step of developing an improved similarity metric based on a vector space , which does not suffer from the document length confound described above . a technique used in information retrieval , though , provides a clue as to how such a metric can be devised . the present invention proposes to use a variation of the technique of singhal et al . 1996 to modify the semantic similarity scores used in natural language processing applications . the main differences from the ir scenario are that the normalization terms will be affected by both documents being compared ( whereas only the length of the indexed document is considered in the ir case ), and that the slope parameter is tuned to optimize a different objective function . rather than minimizing the discrepancy between retrieval and relevance probabilities , the goal in the case of semantic similarity spaces for nlp is to minimize the correlation between document length and the resulting similarity measure . fig3 b illustrates the way in which pivoted normalization is intended to rectify the dependence of semantic similarity estimates on document length . note that in this application , we observe similarity scores for long documents which are generally too high , rather than too low , so that the slope value s will take on a value greater than 1 in this application of pivoted normalization . to evaluate the effect of using pivoted normalization to correct for the confounding effect of document length in vector - based semantic similarity metrics , experiments were conducted on two publicly - available text categorization data sets . the hypothesis to be tested in this evaluation is that providing the classifier with a length - independent measure of semantic similarity will improve performance , because the effects of document length and semantic similarity of vocabulary can be treated independently of one another . in each of the experiments below , the text categorization task was addressed using the j48 decision tree classifier from the weka machine learning toolkit ( witten & amp ; frank 2005 ), a reimplementation of the c4 . 5 classifier ( quinlan 1996 ). the features used in training the classifier were the semantic similarity between the document and the set of training documents belonging to a given topic ( one feature for each topic ), and the number of word types found in the document to be classified ( a measure of document length ). this specification of the task differs from common practice in text categorization , in which each word is treated as an independent feature available to the classifier ( e . g ., gabrilovich & amp ; markovitch 2004 ), and is designed only as a test case for comparing the usefulness of different semantic similarity measures . the results provided below were produced using 10 - fold cross - validation within the evaluation set . the techtc data set ( davidov , gabrilovich , & amp ; markovitch 2004 ) is actually a collection of 100 different test sets for text categorization , each consisting of a set of positive and negative exemplars of a topic . these documents were automatically acquired from the web , and the topics correspond to open directory ( www . dmoz . org ) categories . for the experiments reported here , half of the positive exemplars and half of the negative exemplars were used as the test data for each set , and the remaining exemplars of each type were used to produce the topic vectors to which the test documents were compared . the accuracy statistics reported below reflect the average classification accuracy observed over all 100 topics in the techtc set . the reuters - 21578 text categorization collection ( lewis 2007 ) is a widely used set of reuters newswire articles , categorized into seven topics . in the experiments described below , the “ modified apté ” split ( apté , damerau , & amp ; weiss 1994a ; 1994b ) was used to select documents for use in training ( in this case , defining topic vectors ) and in evaluation . documents labeled as belonging to multiple categories were excluded from the evaluation . the evaluation experiments were carried out for both cva and ri similarity scores , using either the standard cosine normalization term or gtypes ( the geometric mean of the number of word types in the documents compared ). for each of these metrics , a version using pivoted normalization was also considered . in independent experiments using a variety of test corpora , it was determined that the optimal slope parameters for this application did not vary greatly by training corpus . therefore , the slopes used in applying pivoted normalization were s = 5 . 0 for cva , and s = 10 . 0 for ri , values close to the optimal ones . as table 1 shows , with reference to the lexile and lm data sets introduced earlier , the use of pivoted normalization effectively addresses the conflation of semantic similarity with length . for each type of similarity measure , the version using pivoted normalization has a negligible correlation with gtypes . table 2 shows the text classification accuracy of each semantic similarity metric on the reuters and techtc data sets . when pivoted normalization is not used , the models using cva features are more accurate than those using random indexing ( by a wide margin ), and the standard cosine normalization of the similarity scores performs slightly better than the normalization by gtypes . when pivoted normalization is applied , however , these generalizations do not hold . in fact , the best - performing model on both data sets is the one using ri similarity scores normalized by gtypes . the generally superior accuracy of models using normalization by gtypes , among those using pivoted normalization , replicates the finding of singhal , buckley & amp ; mitra 1996 that pivoted normalization tends to work better when combined with a similarity metric normalized by a direct measure of document length , rather than using the standard cosine normalization . comparing the models using pivoted normalization to the standard models , those based on ri similarity scores uniformly show a significant performance improvement when pivoted normalization is applied ( p & lt ; 0 . 05 or better in all cases ). the models based on cva similarity scores , though , show a degradation in performance which is insignificant in most cases , but dramatic in the case of cva similarities with cosine normalization on the reuters data set ( p & lt ; 0 . 01 ). the improvement in classification accuracy from the best model without pivoted normalization to the best model with pivoted normalization is significant for both data sets ( p & lt ; 0 . 05 for reuters , p & lt ; 0 . 01 for techtc ). the significant improvement in classification performance observed for the version of random indexing using pivoted types - normalization demonstrates that this technique can contribute to the performance of nlp applications using vector - based similarity scores . the pivoted version of the random indexing similarity scores provide a conceptually purer representation of the degree to which two documents contain terms similar in meaning , which is more useful in the text categorization tasks used here , and presumably in other nlp tasks as well . a somewhat surprising result is the failure to demonstrate a comparable increase in performance when pivoted normalization is used to calculate cva similarity scores . one explanation for this could be based in the fact that the text categorization tasks used in this paper depend strongly on the exact words found in each documents , rather than just general semantic orientation . this is witnessed by the fact that , of the metrics not using pivoted normalization , the cva - based metrics in table 2 demonstrate the highest performance , and by the fact that it is common practice to use specific words as predictive features for this application . given this , the use of pivoted normalization may degrade the performance on text classification tasks when applied to cva features because it obscures the degree to which two documents contain exactly the same words : long documents with many words in common with the topic vector will have their similarity scores depressed by the technique ( because of the presumption that some of this overlap is simply a coincidental artifact of their length ), while shorter documents with fewer terms in common with the topic vector will have their similarity scores increased . this problem does not arise in applying pivoted normalization to ri similarity scores , because this method does not provide a direct measure of term overlap in the first place . note that the random indexing and content - vector analysis models used in the experiments described above are exemplary and not limiting . embodiments that employ other vector - based similarity calculation techniques are also within the scope of the present invention . the different behavior of pivoted normalization when applied to cva models and ri models was attributed in the discussion above to the fact that random indexing spaces involve dimensionality reduction , which suggests that other reduced - dimensionality techniques like lsa may also be used . this work covers only a portion of the nlp domain affected by the confounding of semantic similarity with document length . an important direction for future work will be to demonstrate that the same improvement which pivoted normalization exhibits for random indexing on these text categorization tasks is also demonstrated for different vector - space techniques and nlp problems . the effectiveness of vector - space methods using pivoted normalization should be tested on additional tasks , such as document clustering and the automated scoring of essays . another open area for future exploration is the use of other methods for addressing the confound of document length . while the pivoted normalization method has become widely used in information retrieval , certainly other ways of addressing the problem are conceivable ( such as the vector translation method suggested by higgins ( 2007 )). while the particular system and method for handling the confounding effect of document length on vector - based similarity scores as herein shown and described in detail is fully capable of attaining the above - described objects of the invention , it is to be understood that it is the presently preferred embodiment of the present invention and is thus representative of the subject matter which is broadly contemplated by the present invention , that the scope of the present invention fully encompasses other embodiments which may become obvious to those skilled in the art , and that the scope of the present invention is accordingly to be limited by nothing other than the appended claims , in which reference to an element in the singular means “ at least one ”, not “ only one ”, unless otherwise stated in the claim . all structural and functional equivalents to the elements of the above - described preferred embodiment that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims . moreover , it is not necessary for a device or method to address each and every problem sought to be solved by the present invention , for it to be encompassed by the present claims . furthermore , no element , component , or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element , component , or method step is explicitly recited in the claims . no claim element herein is to be construed under the provisions of 35 u . s . c . § 112 , sixth paragraph , unless the element is expressly recited using the phrase “ means for ” or , in the case of a method claim , the element is recited as a “ step ” instead of an “ act ”. all publications mentioned herein are incorporated by reference in their entireties . nothing herein is to be construed as an admission that the embodiments described herein are not entitled to antedate such disclosure by virtue of prior invention . as used herein , the term “ comprising ” means “ including , but not limited to .” the invention is a general purpose computer programmed according to the inventive steps herein . the invention can also be embodied as an article of manufacture — a machine component — that is used by a digital processing apparatus and which tangibly embodies a program of instructions that are executable by the digital processing apparatus to undertake the present invention . this invention is realized in a critical machine component that causes a digital processing apparatus to perform the inventive method steps herein . the invention is also a computer - 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