JP2013084136A - Semantic analysis device and program thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To highly accurately construct a model for a semantic analysis even from further few correct instances.SOLUTION: A correct data storage unit stores correct data corresponding to learning data. A constraint condition storage unit stores conditions between latent variables relating to the learning data as constraint condition data. An analysis unit reads the learning data and outputs analysis result data obtained by analyzing the learning data. A model generation unit corresponds the latent variables to the analysis result data using the correct data read from the correct data storage unit and the analysis result data output from the analysis unit and generates a model by performing learning processing using the constraint condition data as the constraint conditions between the latent variables on the basis of the constraint condition data read from the constraint condition storage unit.

Description

  The present invention relates to a semantic analysis device and a program for analyzing the meaning of a sentence.

It is desirable to improve sentence processing efficiency by automatically analyzing the meaning of a large number of sentences. For example, when a large number of sentences with reputations for products, services, broadcast programs, etc. are received, first of all, the meaning of those sentences is first automatically classified automatically to analyze them. Analysis can be performed efficiently.
When analyzing the meaning of expressions such as language, image, video, etc., a technique of extracting those features and determining the meaning based on the features can be considered. At that time, it is conceivable to use not only the surface layer of language, image, video, etc. but also latent variables.

For example, Patent Document 1 discloses a technique for performing topic identification using a latent variable in order to remove a non-term.
Patent Document 2 discloses a technique that uses latent variables for video clustering in a video summarization system.
Patent Document 3 discloses modeling in which a relatively low-dimensional latent variable is placed between an image feature amount extracted from an image and a posture to be output in a system for estimating the posture of a human body by image recognition. A technique is disclosed.
Non-Patent Document 1 discloses a method of using a conditional random field having hidden variables when classifying sentence evaluation polarities.

JP 2010-055553 A JP 2009-095013 A JP 2009-146405 A

Tetsuji Nakagawa, Kentaro Inui, Ikuo Kurohashi, “Evaluation Classification of Dependent Structure Trees with Conditional Random Fields with Hidden Variables”, IPSJ SIG Technical Report, Vol.2009-NL-192, No. 10, 2009

  However, when trying to construct a model that represents the relationship between expression and meaning using latent variables, there is a problem that the model cannot be learned well if there are few correct examples.

  The present invention has been made on the basis of the above-mentioned problem recognition. A semantic analysis apparatus and its program capable of constructing a high-accuracy model and automatically performing semantic analysis from a small number of correct answers are provided. provide.

  [1] In order to solve the above-described problem, a semantic analysis apparatus according to an aspect of the present invention constrains a condition between a correct data storage unit that stores correct data corresponding to learning data and a latent variable related to learning data. A constraint condition storage unit that stores the condition data; a model storage unit that stores model information; an analysis unit that reads the learning data and outputs analysis result data obtained by analyzing the learning data; and the correct answer Using the correct answer data read from the data storage unit and the analysis result data output from the analysis unit, and making the latent variable correspond to the analysis result data, and based on the constraint condition data read from the constraint condition storage unit Then, using the constraint condition data as a constraint condition between the latent variables, a learning process is performed to generate a model, and information on the generated model is generated. The includes a model generation unit to write to the model storage unit.

  According to this configuration, the analysis result data represents information based on the learning data. The correct answer data represents a correct answer corresponding to the learning data. The model generation unit generates a model by performing a learning process based on the analysis result data and the correct answer data. At this time, the model generation unit performs the learning process using the constraint condition data. Since the constraint condition data represents a condition (for example, an equality condition) between latent variables related to the learning data (in other words, the analysis result data), the model generation unit uses the constraint condition data as described above. Performing the learning process acts in a direction in which the condition obtained by the analysis result data and the correct answer data is more strongly restricted. In other words, by storing constraint condition data in advance and using the constraint condition data, the amount of learning data (and correct data) required to construct a model based on the same condition is small. That's it. In other words, a more accurate model can be generated from a small amount of learning data (and correct data). That is, the accuracy when performing semantic discrimination using such a model is improved.

  [2] Further, the semantic analysis apparatus according to one aspect of the present invention stores a correct data storage unit that stores the meaning corresponding to the sentence data for learning as correct data, and stores the similarity between language expressions as constraint data. A constraint condition storage unit for storing, a model storage unit for storing model information for semantic determination of sentence data, reading the sentence data for learning, performing dependency analysis processing of the sentence data, and outputting dependency analysis tree data Using the dependency analysis unit, the correct data read from the correct data storage unit, and the dependency analysis tree data output from the dependency analysis unit, and a latent variable in a node included in the dependency analysis tree data , And based on the constraint condition data read from the constraint condition storage unit, a class between expressions corresponding to a subtree having the node as a root. The relationship as a constraint condition of the latent variable with each other between the node generates a model by performing a learning process, comprising a model generation unit that writes the information of the generated said model to the model storage unit.

  According to this configuration, the dependency analysis tree data represents information based on the sentence data for learning. The correct answer data represents a correct answer corresponding to the sentence data for learning. The model generation unit generates a model by performing learning processing based on the dependency analysis tree data and the correct answer data. At this time, the model generation unit performs the learning process using the constraint condition data. Since the constraint condition data represents a condition (for example, an equality condition) between latent variables related to the sentence data for learning (in other words, the dependency analysis tree data), the model generation unit has the constraint condition data. The use of the above learning process acts in a direction that more strongly restricts the condition obtained by the dependency analysis tree data and the correct answer data. That is, by storing constraint condition data in advance and using the constraint condition data, the amount of learning sentence data (and correct answer data) required to construct a model based on the same level condition is Less is enough. In other words, a more accurate model can be generated from a small amount of learning sentence data (and correct answer data). That is, the accuracy of sentence semantic determination using such a model is improved.

  [3] A semantic analysis apparatus according to an aspect of the present invention includes an input sentence dependency analysis unit that performs dependency analysis processing of input sentence data to be input and outputs dependency analysis tree data, and the input sentence dependency analysis unit. The apparatus further includes a determination unit that determines the meaning of the input sentence data using the dependency analysis tree data output from the analysis unit and the model information read from the model storage unit.

  With this configuration, it is possible to determine the meaning based on the model information stored in the model storage unit for input sentence data whose meaning is unknown. That is, it is possible to analyze the meaning according to the model generated from the learning data and the constraint condition data.

  [4] In addition, according to one aspect of the present invention, a correct data storage unit that stores correct data corresponding to learning data, and a constraint condition storage unit that stores conditions between latent variables related to learning data as constraint condition data A model storage unit that stores model information, an analysis unit that reads the learning data, outputs analysis result data obtained by analyzing the learning data, and the correct data read from the correct data storage unit The analysis result data output from the analysis unit is used to associate a latent variable with the analysis result data, and the constraint condition data is converted to the latent variable based on the constraint condition data read from the constraint condition storage unit. As a constraint condition between each other, a model is generated by performing a learning process, and the generated model information is written in the model storage unit. Is a program for causing a computer to function as a semantic analysis apparatus and a part.

According to the present invention, latent variables are combined by using constraint condition data. As a result, an effect of restraining the model more strongly with a smaller amount of learning data than in the prior art occurs.
Therefore, according to the present invention, a more accurate model for semantic analysis can be generated with a smaller amount of learning data. In addition, using such a model, it is possible to perform a semantic analysis of an input sentence whose meaning is unknown with high accuracy.

It is a block diagram which shows the function structure of the semantic analyzer by one Embodiment of this invention. It is the schematic which shows the structure of the data which a learning data storage part memorize | stores by the embodiment. It is the schematic which shows the structure of the data which the correct data storage part memorize | stores by the same embodiment. It is the schematic which shows the structure of the data which the constraint condition memory | storage part memorize | stores by the same embodiment, and shows the structure of the table showing the relationship between expression ID and expression (headword). It is the schematic which shows the structure of the data which the constraint condition memory | storage part memorize | stores by the same embodiment, and shows the structure of the table showing the relationship between similar expression pair ID and a similar expression pair. It is a flowchart which shows the procedure of the process which performs the machine learning using learning data and produces | generates a model by the embodiment. It is the schematic which shows the example of the dependency analysis result which the dependency analysis part performs by the same embodiment. It is the schematic which shows an example of the structure of the dependency analysis result data which the dependency analysis tree memory | storage part memorize | stores by the same embodiment. It is a flowchart which shows the procedure of the process which analyzes the meaning of an input sentence using a model by the embodiment.

Next, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a block diagram showing a functional configuration of a semantic analyzer according to an embodiment of the present invention. As shown in the figure, the semantic analysis apparatus 1 includes a learning data storage unit 10, a dependency analysis unit 20 (analysis unit), a dependency analysis tree storage unit 30, a model generation unit 40, and a correct data storage unit 50. The constraint condition storage unit 60, the model storage unit 100, the input unit 120, the dependency analysis unit 130 (input sentence dependency analysis unit), the determination unit 140, and the output unit 150 are configured.

The learning data storage unit 10 stores learning data. Specifically, the learning data is learning sentence data.
The dependency analysis unit 20 reads learning data from the learning data storage unit 10 and outputs analysis result data obtained by analyzing the learning data. More specifically, the dependency analysis unit 20 reads learning sentence data, performs dependency analysis processing of sentence data, and outputs dependency analysis tree data. The dependency analysis unit 20 writes dependency analysis tree data obtained as a result of the dependency analysis processing in the dependency analysis tree storage unit 30.
The dependency analysis tree storage unit 30 stores dependency analysis tree data.

  The model generation unit 40 uses the correct answer data read from the correct answer data storage unit 50 and the analysis result data output from the dependency analysis unit 20 to associate the latent variables with the analysis result data, and the constraint condition storage unit 60. Based on the constraint condition data read out from the above, the constraint condition data is used as a constraint condition between latent variables, a learning process is performed to generate a model, and the generated model information is written in the model storage unit 100. Specifically, the analysis result data is analysis tree data obtained as a result of dependency analysis. In addition, the model generation unit 40 associates latent variables with nodes included in the obtained dependency analysis tree data. In addition, the model generation unit 40, based on the constraint condition data read from the constraint condition storage unit 60, for the subtree rooted at the node included in the dependency analysis tree data, the similarity between the representations corresponding to each subtree The learning process is performed using the relationship as a constraint condition between the latent variables between the nodes.

The correct answer data storage unit 50 stores correct answer data corresponding to the learning data. Specifically, the correct answer data is data representing the meaning corresponding to the sentence data for learning.
The constraint condition storage unit 60 stores a condition between latent variables related to learning data as constraint condition data. Specifically, the constraint condition data is data representing a similarity relationship between language expressions.
The model storage unit 100 stores model information for analysis. Specifically, the model storage unit 100 stores model information for semantic determination of sentence data.

The input unit 120 inputs a sentence to be analyzed. The meaning of the input sentence is unknown.
The dependency analysis unit 130 performs dependency analysis processing on the input sentence data input from the input unit 120 and outputs dependency analysis tree data.
The determination unit 140 determines the meaning of the input sentence data using the dependency analysis tree data output from the dependency analysis unit 130 and the model information read from the model storage unit 100.
The output unit 150 outputs the meaning determined by the determination unit 140.

  The semantic analyzer 1 generates a model in advance by using an opinion sentence for a broadcast program as learning data, and classifies the meaning of a newly input unknown sentence (an opinion for the broadcast program). The semantic analysis apparatus 1 can be applied not only to the classification of opinions regarding broadcast programs but also to other tasks (such as opinions regarding a specific theme, opinions regarding products, and opinions regarding services).

  FIG. 2 is a schematic diagram illustrating a configuration of data stored in the learning data storage unit 10. As illustrated, the learning data storage unit 10 stores sentence numbers and learning sentence data in association with each other. The learning data storage unit 10 stores this data in a relational table, for example. In the illustrated example, the learning data storage unit 10 stores the sentence “Mr. Nakamura, I was very impressed” as the first sentence, and “I was deeply impressed with Dr. Nakamura's action” as the second sentence. The sentence is memorized. In this example, the learning data storage unit 10 stores data of L sentences from the first to the Lth. As the learning sentence data, in one example, opinion sentences collected in the past with respect to broadcast programs are used.

FIG. 3 is a schematic diagram illustrating a configuration of data stored in the correct answer data storage unit 50. As illustrated, the correct answer data storage unit 50 stores sentence numbers and sentence meanings (correct answers) in association with each other. The correct answer data storage unit 50 stores the data in a relational table, for example. The sentence number data stored in the correct data storage unit 50 is associated with the sentence number data stored in the learning data storage unit 10. That is, for example, the meaning of the first sentence “Mr. Nakamura was very impressed” stored in the learning data storage unit 10 is s ⁽¹⁾ ₀ stored in the correct data storage unit 50. The same applies to the meaning s ⁽²⁾ ₀ of the second sentence and the meaning s ⁽³⁾ _{0 of the} ^third sentence. The values of s ⁽¹⁾ ₀ , s ⁽²⁾ ₀ ,..., S ^(L) ₀ stored in the correct data storage unit 50 are, for example, finite sets {C ₁ , C ₂ ,. C _N }, which is given in advance by hand corresponding to the sentence data for learning.

  As an example, the meaning can take any element of the set {positive opinion, negative opinion} as a value.

  4 and 5 are schematic diagrams illustrating the configuration of data stored in the constraint condition storage unit 60. FIG. The data stored in the constraint condition storage unit 60 represents a similarity relationship between expressions and is used as a constraint condition in a model generation process to be described later.

  FIG. 4 is a part of data stored in the constraint condition storage unit 60, and shows the configuration of a table representing the relationship between expression IDs and expressions (headwords). As shown in the figure, this table stores various expressions (headwords) in association with expression IDs. Here, the expression is not limited to one word, and may be an expression including a plurality of words. For example, the expression corresponding to the expression ID “k1” is “interesting”, and the expression corresponding to the expression ID “k2” is “interesting”.

  FIG. 5 is a part of data stored in the constraint condition storage unit 60, and shows the configuration of a table representing the relationship between the similar expression pair ID and the similar expression pair. As illustrated, this table stores similar expression pairs in association with similar expression pair IDs. The similar expression pair is expressed as “(k1, k2)”, for example, and k1 and k2 are expression IDs, respectively. That is, for example, the similar expression pair corresponding to the similar expression pair ID “p1” is “(k1, k2)”, which indicates that the expression “interesting” and the expression “interesting” are similar.

  One of the following two methods is used as a method of creating data stored in the constraint condition storage unit 60.

  The first method is a method that emphasizes completeness. In this method, first, an expression is extracted from a dictionary or the like. For example, a list of headwords is extracted from the evaluation expression dictionary. Then, an expression ID is appropriately given to each expression. Next, for each of the obtained expressions (headwords), expressions similar to each other according to external criteria are connected to form a pair, and the expression pair is written out in the table shown in FIG. At this time, a similar expression pair ID is appropriately assigned.

  The second method is a method that emphasizes reality. In this method, first, learning data is subjected to dependency analysis, and a conventional model is generated from the dependency analysis result. Then, a character string corresponding to the partial structure tree having the latent variable appearing in the model as a root is used as a list of headwords. Hereinafter, similarly to the first method, an expression ID is appropriately given to each expression. Then, for each of the obtained expressions (headwords), expressions similar to each other according to an external criterion are connected to form a pair, and the expression pair is written in the table shown in FIG. At this time, a similar expression pair ID is appropriately assigned.

  The constraint condition storage unit 60 stores many similar relationships between phrases. The constraint condition storage unit 60 stores data created in advance based on a synonym dictionary, a synonym expression dictionary, and the like.

Next, the operation of the semantic analyzer 1 will be described.
FIG. 6 is a flowchart illustrating a processing procedure in which the semantic analysis apparatus 1 performs machine learning using learning data and generates a model. Hereinafter, it demonstrates along this flowchart.
First, in step S <b> 1, the dependency analysis unit 20 performs dependency analysis processing on each of the L sentences stored in the learning data storage unit 10, and writes the result in the dependency analysis tree storage unit 30. Specifically, the dependency analysis unit 20 performs phrase chunking for each sentence, and analyzes the dependency relationship between the chunks. The dependency analysis process itself can be performed using an existing technique. For example, CaboCha (Yet Another Japanese Dependency Structure Analyzer) can be used as a Japanese dependency analyzer based on a support vector machine (SVM). The dependency analysis unit 20 writes the result of the dependency analysis process in the dependency analysis tree storage unit 30.

  FIG. 7 is a schematic diagram illustrating an example of a dependency analysis result by the dependency analysis unit 20. FIG. 5A shows the result of dependency analysis of the first sentence “Mr. Nakamura, I was very impressed” stored in the learning data storage unit 10. In (a), “Mr. Nakamura” is related to “I was impressed” and “Very” is related to “I was impressed”. FIG. 5B shows the result of the dependency analysis of the second sentence “I was deeply impressed by Dr. Nakamura's action”. In (b), “Nakamura's” is related to “behavior”, “behavior” is related to “received”, “deep” is related to “impressed”, and “impressed” is received. "."

  As described above, the dependency analysis result has a tree structure of clause chunks. In the figure (a), “I was impressed” corresponds to the root of the tree structure. In FIG. 5B, “received” corresponds to the root of the tree structure. “Affirmative opinions” attached to each of (a) and (b) in the figure represent the meaning of each sentence. The meaning of the entire sentence is given in advance, and as described above, the correct answer data storage unit 50 stores the meaning of each sentence.

The dependency analysis result data stored in the dependency analysis tree storage unit 30 is expressed as follows. That is, for L sentences included in the learning data, w ^(l) is a vector of words appearing in the l-th sentence. However, 1 ≦ l (el) ≦ L. In addition, the i-th word (or phrase chunk, which is the same in the case of “word” hereinafter) in the l-th sentence is w ^(l) _i . H ^(l) is a vector representing the dependency structure of the l-th sentence. h ^(l) _i , which is an element of h ^(l) , is a numerical value representing a word related to the i-th word in the l-th sentence. When the i-th word corresponds to the root node of the dependency structure tree, h ^(l) _i uses a predetermined value such as null or zero.

FIG. 8 is a schematic diagram illustrating an example of the configuration of dependency analysis result data stored in the dependency analysis tree storage unit 30. As shown in the figure, the dependency analysis result data is tabular data, and includes items of a sentence number, a word vector, and a dependency structure vector. For example, in the data on the l-th row, the sentence number is l (el), the word vector is w ^(l) , and the dependency structure vector is h ^(l) . The word vector w ^(l) and the dependency structure vector h ^(l) are as described above. That is, the data of each line in the dependency analysis result data has information equivalent to the dependency analysis tree of each sentence.

Returning to FIG. 6, when the dependency analysis process is completed, first, in step S <b> 2, the model generation unit 40 stores the dependency analysis result data from the dependency analysis tree storage unit 30, that is, w ^(l) and h ^{( Read} the data of ^l) .

  In step S <b> 3, the model generation unit 40 reads out correct data of each sentence from the correct data storage unit 50. Correct data is the meaning of each sentence.

  In step S <b> 4, the model generation unit 40 reads constraint condition data from the constraint condition storage unit 60.

In step S <b> 5, the model generation unit 40 generates a model using latent variables and writes the model in the model storage unit 100. Here, s ^(l) is a vector of latent variables of the l-th sentence. S ^(l) _i is a latent variable located in the i-th word in the l-th sentence, and is a model that bears the meaning of the partial dependency structure tree rooted at the node. . Meaning s ^(l) _i is, for example, any element of a finite set. As an example, as described above, the meaning s ^(l) _i can take any element of the set {positive opinion, negative opinion} as a value. P (s ^(l) | w ^(l) , h ^(l) ) is a latent variable model for the l-th sentence. The meaning of this sentence as a whole is obtained by reading from the correct answer data storage unit 30 and is defined as a variable s ^(l) ₀ . What is obtained from the correct data storage unit 30 is only the meaning of the entire l-th sentence, and other latent variables are left unobserved.

In this model, the probability that the meaning of the l-th sentence is p ^(l) is expressed by the following equation (1).

And the model production | generation part 40 performs learning by likelihood maximization based on the given learning data and correct answer data, and estimates a model parameter. L sentences w ^(l) and h ^(l) (where 1 ≦ l (el) ≦ L) and L correct answers p ^(l) (also 1 ≦ l (el) ≦ L) were given. The overall likelihood R is expressed by the following equation (2).

  That is, it is as expressed in the following formula (3).

  That is, the model generation unit 40 estimates model parameters using the constraint condition shown in the following equation (4).

  In addition to the condition of Expression (4), the model generation unit 40 also uses the constraint conditions read from the constraint condition storage unit 60 to estimate model parameters. The constraint condition storage unit 60 stores a large amount of information related to expressions having similar relationships, but the model generation unit 40 uses a dependency analysis tree based on the result of dependency analysis of learning data. Select and read constraint conditions for latent variables corresponding to nodes. When the constraint condition is generalized, it is expressed as the following equation (5).

  Equation (5) is the meaning of the partial dependency structure tree with the j-th word in the i-th sentence as the top, and the meaning of the partial dependency structure tree with the m-th word in the k-th sentence as the top. Represents the condition that and are the same. The fact that a certain subtree is the same as another subtree is expressed as the similar expression pair described in FIG. In the example of FIG. 4, the data of the similar expression pair ID “p3” indicates that the expression k5 “I am impressed” is similar to k6 “I am impressed”. The model generation unit 40 reads all applicable similar relationships between subtrees from the constraint condition storage unit 60 and uses them as constraint conditions during learning (parameter estimation).

  The specific operation procedure of the model generation unit 40 is as follows.

The model generation unit 40 first generates a model represented by a structure of LDCRF (Latent-Dynamic Conditional Random Field). LDCRF itself is an existing machine learning method, and the following two points are extended with respect to CRF. The first point can handle vectors s ^(l) , w ^(l) , and h ^(l) having different numbers of elements depending on l (el) (dynamic model Dynamic Model). Second, latent variables have been introduced (latent variable model Latent Model). This model has a tree structure, and model parameter estimation methods are known.

  Next, the model generation unit 40 adds a constraint condition to the tree structure model based on the constraint condition read from the constraint condition storage unit 60. The constraint condition here is to combine latent variables at different positions of the tree structure generated by the model generation unit 40 above. At this time, the tree structure is converted into a general graph structure by combining latent variables.

  Even in a general graph structure model, model parameters can be estimated by the maximum likelihood method. Maximum likelihood estimation of model parameters is performed according to the general theory of graphical models.

  However, a general graph structure model may include a loop structure. There is no efficient estimation algorithm for performing maximum likelihood estimation of model parameters for a model having a graph structure including a loop structure. Therefore, when a loop occurs in the model as a result of applying all the constraint conditions, a means for avoiding the loop may be taken for efficient maximum likelihood estimation.

  An example of loop avoidance means is as follows. That is, the constraint conditions are applied from the order close to the top (root) of the tree structure, and when a loop is generated by trying to combine latent variables applied later, the constraint conditions are excluded. Such means has a function of giving priority to the connection close to the top of the tree structure (parse tree) and excluding those that generate a loop in the connection far from the top.

  The likelihood R expressed by equation (3) depends on a number of parameters. If the vector of these parameters as a whole is represented by Θ, the following equation (6) is obtained.

  The model generation unit 40 calculates Θ that maximizes R in Expression (6) under the constraint condition (s) expressed in the form of Expression (5). That is, the model generation unit 40 calculates Θ (hat) in the following equation (7).

  Then, the model generation unit 40 writes the parameter value data (Θ (hat)) obtained by learning into the model storage unit 100. The semantic analysis apparatus 1 ends the process for generating the model.

Next, a procedure of processing for determining the meaning of an input sentence in a state where a model has already been generated will be described. FIG. 9 is a flowchart showing a procedure of processing for analyzing meaning using a model. Hereinafter, it demonstrates along this flowchart.
First, in step S21, the input unit 120 reads input sentence data.

Next, in step S <b> 22, the dependency analysis unit 130 performs dependency analysis processing on the input sentence read by the input unit 120. The function of the dependency analysis unit 130 is the same as that of the dependency analysis unit 20. The dependency analysis unit 130 outputs a word vector w ⁽ⁱⁿ⁾ and a dependency structure vector h ⁽ⁱⁿ⁾ corresponding to the input sentence as a result of the dependency analysis process. The i-th element w ⁽ⁱⁿ⁾ _i of the word vector w ⁽ⁱⁿ⁾ is the i-th output word (or phrase chunk in the input sentence. The same applies in the case of “word” below). The i-th element h ⁽ⁱⁿ⁾ _i with the dependency structure vector h ⁽ⁱⁿ⁾ is a numerical value representing the word to which the i-th word is related in the input sentence.

Next, in step S <b> 23, the determination unit 140 determines the meaning of the input sentence based on the word vector w ⁽ⁱⁿ⁾ and the dependency structure vector h ⁽ⁱⁿ⁾ output from the dependency analysis unit 130. At this time, the discriminating unit 140 reads the model whose parameter has been estimated from the model storage unit 100 based on the learning data, and applies the word vector w ⁽ⁱⁿ⁾ and the dependent structure vector h ⁽ⁱⁿ⁾ to the model. Calculate the meaning of the input sentence. The meaning value calculated by the determination unit 140 is, for example, _one of the elements of a finite set {C ₁ , C ₂ ,..., C _N }. As an example, the calculated meaning value is one of the elements of the set {positive opinion, negative opinion}.

  Finally, in step S24, the output unit 150 outputs the meaning (maximum likelihood meaning) of the input sentence calculated by the determination unit 140 based on the model.

  The operation of the semantic analyzer 1 is summarized as follows. The semantic analysis apparatus 1 first constructs LDCRF for each node of the dependency structure tree as a technique for modeling the meaning of the partial dependency structure tree of the node with a latent variable. Next, a synonym dictionary (including a synonym dictionary, a thesaurus, and an ontology) is used. When it is known that a partial expression in a sentence has the same meaning (including a similar case) as another partial expression, this is used as constraint condition data. A large amount of example sentences are given as learning data to the model constructed by this procedure, and model parameters are estimated by a machine learning method. At this time, the meaning of each example sentence (meaning of the whole sentence) is separately provided as correct answer data. When LDCRF is simply used, different expressions are learned separately even if they are semantically similar. The semantic analysis apparatus 1 according to the present embodiment can perform model learning efficiently by combining the latent variables of model learning based on the above constraint condition data. The combination of the latent variables is to give a constraint condition between the latent variables by the equation (5).

  By adopting such a configuration, it is possible to construct an accurate model even from a relatively small amount of correct answer cases.

[Comparison with other methods]
(1) Simple character string replacement In addition, in constructing a model, a method of replacing similar expressions may be considered. However, in the configuration of the semantic analysis apparatus 1 according to the present embodiment, the replacement is more than the case of replacement. It is possible to build a model with better accuracy while maintaining rich expressions.

  For example, consider a case where the following sentence set is classified as a processing target. That is, “The story of the person named ○○ is not interesting”, “The story of the person named Δ △ is interesting”, “The story of the person named XX is boring”, “I want the story of the person named ** to be interesting” It is a sentence set. When such a sentence is classified, which sentence is considered to be similar depends on the application. Consider the case where an application task counts the proportion of positive and negative opinions in a broadcast program reputation analysis system. In such a task, “... I want you to be interesting” should be treated as a negative opinion, just like “... I do n’t like it”. The reason is that “... please make it interesting” implies (implicitly assumed) “not interesting”. In other words, the two phrases “I want you to be interesting” and “I am not interesting” need to be treated as similar phrases. It also depends on the task.

  If a similar phrase replacement method is used, “not interesting” is replaced with “not boring”. Then, the clue that the expression “I want you to be interesting” should be treated as equivalent to “I am not interesting” is lost. In other words, when the method of replacing similar phrases is adopted, there is a restriction on the use of similar expressions, whereas the semantic analyzer 1 according to this embodiment is a means of combining latent variables while maintaining various expressions. Giving a constraint condition with the above has an advantageous effect.

  In addition, as described above, "... I want you to do" (for example, "I want you to be interesting") and "... No" (for example, "I don't like") are treated equally (similarly) Rules specific to application tasks. There can be both a method of manually providing a rule and a case where a model automatically machine-learned from learning data performs an action equivalent to that rule, but both are the same. In this case, if you try to classify the sentence “I want you to make the story of ** interesting” into either a positive opinion or a negative opinion, the same learning data does not exist. And the process is left to the model. From learning data examples, when learning is made that "... please do" and "... no" have the same meaning, "I want you to be interesting" A model that can be treated as equivalent to “not interesting” can be constructed. The semantic analysis apparatus 1 according to the present embodiment performs such learning. On the other hand, if you replace character strings in advance with learning data, the sentence “not interesting” disappears from the learning data, so how to classify “not interesting” after learning As a result, there is no clue as to what should be done, and accurate classification as in this embodiment cannot be performed.

(2) Character string replacement after inference As another technique, it is conceivable to perform a method of performing character string replacement after inferring input data to be processed.
However, in this case, for example, a sentence “XX is not funny or interesting” is converted into a sentence “XX is not funny” by character string replacement. The expression resulting from the replacement of the character string is analyzed as an opinion sentence having both a positive expression “funny” and a negative expression “dull”, and does not lead to an accurate classification. This phenomenon is called a scope problem, and a simple character string replacement cannot correctly handle the scope of the negative expression “not”, thereby causing a part whose meaning changes after character string replacement. A problem similar to the problem that occurs in the expression “to not” also occurs in expressions such as “too much”, “not enough”, and “to tend to”.

  In addition, an expression with a conditional clause such as “It was interesting if ...” implies a negative sentence, and the same problem arises. For example, there are some rhetorical polite examples such as “I wish I had another idea”, and there are some examples that seem like “I need another idea.” To analyze the meaning In some cases, it is necessary to pay attention not only to the expression of the part “to ni” but also to the expression of the part “to if”. Even if the expression "if it is interesting" and the expression "not interesting" are treated as similar expressions, if the former is replaced with the latter, the conditional clause "if it" Since parts are no longer replaced, important information is missing.

(3) Superiority of the present embodiment As described above, the method of performing character string substitution can cause side effects such as missing information and modification of meaning. On the other hand, the technique according to the present embodiment performs processing by combining latent variables without modifying the input sentence, that is, using a constraint condition between subtrees. When multiple items are described in one sentence, the process of determining which part has an important influence on the overall meaning should be made at the end instead of using local judgment. desirable. For this reason, the method according to the present embodiment, which performs processing using the learned model while leaving all the information of the original sentence, is more advantageous than the method of character string replacement that performs processing only by partial / local judgment. The result is obtained.

  Note that the functions of the semantic analysis apparatus in the above-described embodiment may be realized by a computer. In that case, the program for realizing the control function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed. Here, the “computer system” includes an OS and hardware such as peripheral devices. The “computer-readable recording medium” refers to a storage device such as a flexible disk, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Furthermore, a “computer-readable recording medium” dynamically holds a program for a short time, like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. In this case, a volatile memory inside a computer system serving as a server or a client in that case may be included, and a program that holds a program for a certain period of time. The program may be a program for realizing a part of the above-described functions, or may be a program that can realize the above-described functions in combination with a program already recorded in a computer system.

Although a plurality of embodiments have been described above, the present invention can also be implemented in the following modifications.
For example, although the configuration example of the dependency analysis result data stored in the dependency analysis tree storage unit 30 is illustrated, the dependency relationship (tree structure) between clauses may be expressed in another form as the data structure. good.
Further, for example, in the processing procedure shown in FIG. 6, the order of steps S2, S3, and S4 is arbitrary, and the order may be changed.
In addition, a device for generating a model (learning data storage unit 10, dependency analysis unit 20, dependency analysis tree storage unit 30, model generation unit 40, correct answer data storage unit 50, constraint condition storage unit) 60, a model storage unit 100), a device configuration for sentence determination using the generated model (model storage unit 100, input unit 120, dependency analysis unit 130, determination unit 140, output unit 150) may be realized as separate devices.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes designs and the like that do not depart from the gist of the present invention.

  The present invention can be used, for example, for efficiently and automatically classifying the meaning of a large number of sentences. For example, it can be used to efficiently classify opinions from users of products and services. For example, it can be used to efficiently classify opinions from viewers of broadcast programs.

1 Semantic Analyzer 10 Learning Data Storage 20 Dependency Analysis Unit (Analysis Unit)
30 dependency analysis tree storage unit 40 model generation unit 50 correct data storage unit 60 constraint condition storage unit 100 model storage unit 120 input unit 130 dependency analysis unit (input sentence dependency analysis unit)
140 Discriminating unit 150 Output unit

Claims (4)

A correct data storage unit that stores correct data corresponding to the learning data;
A constraint storage unit that stores conditions between latent variables relating to learning data as constraint data;
A model storage unit for storing model information;
An analysis unit that reads the learning data and outputs analysis result data obtained by analyzing the learning data;
Using the correct answer data read from the correct answer data storage unit and the analysis result data output from the analysis unit, the latent data is associated with the analysis result data, and the constraint condition data read from the constraint condition storage unit Based on the constraint condition data as a constraint condition between the latent variables, generating a model by performing a learning process, and writing the generated model information in the model storage unit,
A semantic analysis device comprising: A correct data storage unit for storing the meaning corresponding to the sentence data for learning as correct data;
A constraint storage unit that stores similarity relationships between language expressions as constraint data;
A model storage unit for storing model information for semantic determination of sentence data;
A dependency analysis unit that reads the sentence data for learning, performs dependency analysis of the sentence data, and outputs dependency analysis tree data;
Using the correct data read from the correct data storage unit and the dependency analysis tree data output from the dependency analysis unit, a latent variable is associated with a node included in the dependency analysis tree data, and the constraint Based on the constraint condition data read from the condition storage unit, a model is obtained by performing a learning process using a similarity relationship between expressions corresponding to subtrees rooted at the node as a constraint condition between the latent variables between the nodes. Generating a model, and writing the generated model information into the model storage unit,
A semantic analysis device comprising: An input sentence dependency analysis unit that performs dependency analysis processing of input sentence data to be input and outputs dependency analysis tree data;
A determination unit for determining the meaning of the input sentence data using the dependency analysis tree data output from the input sentence dependency analysis unit and the model information read from the model storage unit;
The semantic analysis device according to claim 1, further comprising: A correct data storage unit that stores correct data corresponding to the learning data;
A constraint storage unit that stores conditions between latent variables relating to learning data as constraint data;
A model storage unit for storing model information;
An analysis unit that reads the learning data and outputs analysis result data obtained by analyzing the learning data;
Using the correct answer data read from the correct answer data storage unit and the analysis result data output from the analysis unit, the latent data is associated with the analysis result data, and the constraint condition data read from the constraint condition storage unit Based on the constraint condition data as a constraint condition between the latent variables, generating a model by performing a learning process, and writing the generated model information in the model storage unit,
A program that causes a computer to function as a semantic analysis device.

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