JPH09212511A - Natural language processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a correct process result without making the same mistake again by rewriting the priority of priority information on an element which derives the process result according to whether the process result of a process part is correct or incorrect. SOLUTION: An analysis result is displayed by an update learning part 7 and if a user judges that the modification of one paragraph is incorrect, the user selects a rule which seems corrects out of grammatical rules which are already stored in a result storage part 6 or inputs a new grammatical rule. Then the priority flag 5C of the current rule is set OFF. The priority flag 5C of the rule which gives correct modification, on the other hand, is set ON. When the rule is updated and learnt, information showing whether or not the priority flag TC is ON or OFF effectively operates in next and succeeding analyzing processes. The load of a calculation quantity is smaller when the priority flag 5C is re-set ON or OFF than when the probability of a rule is changed at each time, and the immediateness of learning effect is higher.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a natural language processing apparatus, and can be applied to, for example, a machine translation system or a natural language interface.

[0002]

2. Description of the Related Art Generally, in conventional natural language processing, completely correct analysis processing and generation processing are not guaranteed due to the ambiguity of natural language. Therefore, conventionally, various research institutions have been researching techniques for eliminating this ambiguity. Note that, as such a technique, for example, a document (Keh-Yih SU and Jing-Shin CHANG: SEMANTIC
AND SYNTACTIC ASPECTS OF SCORE FUNCTION, pp642-644,
COLING88,1988). The technique disclosed in this document is to assign a value such as a probability to a grammar rule, and preferentially use the one with a higher probability in the analysis process or the generation process.

[0003]

However, in the case of the above technique, every time the analysis process or the generation process is performed, the probability of changing the analysis result or the rule included in the generation result, which is the cause of the erroneous determination, is changed. There is a problem that the load of the calculation amount becomes very large. Further, even if the probability is changed according to the result, there is no guarantee that the changed result will immediately exert an effect in the next analysis processing or generation processing, so there is still a problem in terms of immediate effect.

Therefore, it is desired to realize a natural language processing method which has a smaller calculation load and is excellent in immediate effect.

[0005]

In order to solve such a problem, the present invention relates to a storage device for storing two pieces of information, probability information and priority information, for each element constituting a natural language processing rule, and a language resource. When the processing result of the processing unit that processes the input language by using the element of is incorrect, and the processing result of the processing unit is incorrect, the priority of the priority information of the element that derives the result is rewritten to be low, and the processing of the processing unit is changed to the correct result. A natural language processing device is configured by an update learning unit that rewrites the priority information of the derived element to a higher priority.

As a result, when an input sentence having the same structure as an input sentence that has been erroneous in the previous process is input, the element whose priority information has been rewritten to have a high priority in the previous process is used. It will be selected in the processing, and the correct processing result can be obtained without repeating the same error twice.

The burden of the calculation amount relating to the rewriting of the priority information is small, and since it is effective from the next processing, immediate effect is guaranteed.

Further, according to the present invention, two pieces of probability information and frequency information are provided for each element constituting the natural language processing rule.
A storage device that stores one piece of information, a processing unit that processes an input language using elements of language resources, and when the processing result of the processing unit is incorrect, the frequency that represents the number of times the element that derives the result is adopted. When the processing result of the processing unit is correct, the value of the information is reduced, and the natural language processing device is configured with the update learning unit that increases the value of the frequency information indicating the number of times the element that derives the result is adopted. To do.

In this way, when one process is completed, only the frequency information is rewritten, not the probability information, so that the burden on the processing unit can be reduced as compared with the case where the probability information is recalculated and rewritten. The speed can be improved.

Further, according to the present invention, for each element constituting the natural language processing rule, a storage device for storing two pieces of information, probability information and reliability information, and a processing for processing an input language by using an element of a language resource. The natural language processing device is configured by a processing unit and an update learning unit that reduces the reliability of reliability information corresponding to an element that derives the result when the processing result of the processing unit is likely to be incorrect. To do.

Since the element having low reliability of the reliability information can be prevented from being used in the processing of the input language from the next time onward, the accuracy of the processing result can be improved accordingly.

[0012]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of a natural language processing method and apparatus according to the present invention will be described in detail below with reference to the drawings.

(1) Configuration of Natural Language Processing Device FIG. 1 is a block diagram showing a schematic configuration of a natural language processing device according to this embodiment. This natural language processor 1
Is actually composed of an input device such as a keyboard and a mouse, an output device such as a display and a printer, and an information processing device such as a workstation and a personal computer equipped with auxiliary storage devices such as a hard disk as peripheral devices. However, if the structure is made into a functional block from the natural language processing function, it becomes as shown in FIG.

The natural language processing apparatus 1 has an input unit 2,
Processing unit 3, dictionary storage unit 4, rule storage unit 5, result storage unit 6
And the update learning unit 7.

Of these, the input unit 2 is used for inputting a natural language or an intermediate language as an input language. For example, in the case of analysis processing, the natural language itself such as "put a book on the desk." Is input, and in the case of generation processing, "desk-place-put" or "book-target-put" is said. An intermediate language is entered. In addition to this, the input unit 2 is also used to input an instruction to individually correct the information associated with the rule used in the analysis process or the generation process.

The processing unit 3 analyzes the natural language input using the dictionary stored in the dictionary storage unit 4 and the rules stored in the rule storage unit 5, or processes the natural language from the intermediate language. It is a part. The processing result of the processing unit 3 is stored in the result storage unit 6 together with all the rules used at the time of processing, and can be read as needed at the time of checking.

The dictionary storage unit 4 is a unit for storing a dictionary which is a set of dictionary items. Here, the dictionary item defines information about one word, and describes the grammatical property and the semantic property of the word.

The rule storage unit 5 is a part for storing language resources such as analysis rules and generation rules used in analysis processing and generation processing. Each element of the language resource stored in the rule storage unit 5, that is, each of the analysis rule and the generation rule, has probability information 5A, frequency information 5B, and priority flag 5C.
And four pieces of information of the trust flag 5D are associated with each other, and can be used when deciding a rule to be applied in the analysis processing and the generation processing.

Incidentally, the probability information 5A is information representing the probability that the corresponding rule will be applied. Also, frequency information 5B
Is information indicating the number of times the corresponding rule is applied. The priority flag 5C is information indicating the high priority, and when it is on, the priority is high, and when it is off, the priority is low. further,
The reliability flag 5D is information indicating a high degree of reliability, and indicates that there is a high possibility that it is wrong when it is on, and a high probability that it is certain when it is off.

When the user checks the processing result after the processing is finished, the update learning unit 7 reads out the processing result from the result storage unit 6 and all the rules used in the process of obtaining the result, and displays them on the screen. This is the part to be displayed. Then, the update learning unit 7 is configured to rewrite or add necessary information when the user instructs to update or newly register the information of each rule stored in the rule storage unit 5. . In addition, the update command or the registration command at this time is input unit 2
Is given to the update learning unit 7 from the processing unit 3 that operates based on the input of. At this time, there are the following five types of specific processing executed by the update learning unit 7.

The first process is a process for newly registering because a correct rule does not exist in the rule storage unit 5. When a correct rule is newly registered in this process, at the same time, the probability information 5A of the rule is set to NULL (reset state), the frequency information 5B is set to "1", and the priority flag 5C is set to ON. , The confidence flag 5D is set to off.

The second process is a process of updating only the information of the current rule which is highly likely to be erroneous. In this process, the value of the frequency information 5B of the current rule is decreased by "1", the priority flag 5C is turned off, and the confidence flag 5D is turned on.

The third process is a process for updating the current rule based on the fact that there is a more appropriate rule than the current rule. In this process, the priority flag 5C of the current rule is turned off.

The fourth process is a process of updating the information selected when the rule selected as more appropriate than the current rule exists in the update storage unit 5. In this rule, frequency information 5
B is incremented by "1", the priority flag 5C is turned on, and the trust flag 5D is turned off.

The fifth process is a process in which nothing is done while maintaining the current state.

(2) Example of processing operation Hereinafter, how the natural language processing apparatus 1 according to this embodiment processes a natural language and learns rules will be specifically described.

However, since the principle operation of the analysis process and the generation process is basically the same, that is, the same rule is selected in each case from the plurality of candidates, the following is shown in FIG. Description will be given based on the natural language analysis processing procedure shown.

First, when a natural language is input from the input unit 2 in units of 1 minute, as shown in step SP1, the processing unit 3
Starts the analysis of the input sentence using the dictionary stored in the dictionary storage unit 4 and the rules stored in the rule storage unit 5.
Here, it is assumed that "He brought a boy in black clothes." Is input as the input sentence.

In the processing unit 3, as shown in FIG. 3, rules stored in advance in the rule storage unit 5 are used to determine the structure of which bunsetsu that constitutes an input sentence modifies which bunsetsu by what kind of relation. Analyze using. In this case, the modified relationship depends on the actor's relationship.
There is a relation 12 relating to “he is” brought in, and which is to be applied is determined by the processing unit 3 using the probability information 5A and the like. However, all the rules used to analyze this input sentence are stored in the result storage unit 6. That is, a rule set {Rα1, Rα2, ...
...} and a rule set {Rβ1, Rβ2, which gives a relation 12]
......} are all stored. Note that the number of elements in the rule set depends on the grammar used for analysis, and may be one. If there is no applicable rule, it will be an empty set.

There are two methods for checking the analysis results obtained in this way: a method of interactively checking each sentence and a method of collectively checking the analysis results of a plurality of sentences. However, in any case, the analysis result for the input sentence is the same in that it is stored in the result storage unit 6.

Now, from here onward, it is assumed that the relation 11 shown by the solid line in FIG. 3 is adopted as the analysis result in the processing unit 3.

When this analysis is completed, the process proceeds to step SP2, and the relation 11 as the analysis result is displayed on the display screen (not shown) by the update learning unit 7. Whether or not the analysis result is correct is inquired to the user in sequence for each clause in the next step SP3.

Here, if the dependency relations about all the phrases constituting one sentence (for example, the dependency rules Rαi (i = 1, 2, ...) For the phrases of “he is” ”are correct, an affirmative result is obtained. Then, the process returns to step SP1 and shifts to the processing for the next input sentence. At this time, if the user gives an instruction to update the frequency information, in step SP11, "1" is added to the frequency information 5B of the rule that gives the correct dependency relationship.

On the other hand, when the user determines that the dependency relation of a certain clause is wrong, a negative result is obtained and the process proceeds to step SP4, where the user selects a more correct grammar rule. Alternatively, the process waits for input. For example, it is correct to put the phrase “he is” in “brought in.” Instead of “worn.”

In step SP4, the user selects a rule that seems to be correct from the grammatical rules already stored in the result storage unit 6 or inputs a new grammatical rule.

[0036] In step SP5, the process of inquiring thus modified is the rule applied to either the user to record in any condition for take future analysis processing is performed.

In this step SP5, first, four kinds of selections are made. That is, whether the entered rule is newly registered (step SP6) or the information is updated only for the current rule that is likely to be incorrect (step SP7).
The current rule is correct, but there is a more appropriate rule. Therefore, the current rule is updated (step SP8) or the next clause is judged without doing anything.

The current rule is correct, but there is a more appropriate rule. Therefore, when updating the current rule, the following two selections are made depending on whether or not the appropriate rule exists in the rule storage unit 5. One is a process of updating an existing rule when a more appropriate rule (learning rule) exists in the rule storage unit 5, and one is a more appropriate rule (learning rule) stored in the rule storage unit 5. This is the process of newly registering the rule even when it does not exist.

For example, in the case of this example, "he is" in relation 11
Is not appropriate, and since the dependency of relation 12 is selected in step SP4, step SP4
Process 8 is selected. At this time, the update learning unit 7 operates as shown in FIG.
, The priority flag 5C of the current rule Rαi before updating
Set to off.

On the other hand, in this example, since the relation 12 that gives the correct dependency already exists in the rule storage unit 5, a positive result is obtained in step SP9 provided after this step SP8, and the process proceeds to step SP10. Proceed to. Then, the frequency information 5B of the rule Rβj is increased by “1” from “Fβj” to “Fβj + 1”, the priority flag 5C is turned on, and the reliability flag 5D is turned off.

In the above example, since the rule that gives the correct dependency relationship exists in the rule storage unit 5, step SP
Although the process proceeds from step 9 to step SP10, but if it does not exist, the process proceeds from step SP9 to step SP6, and the probability information 5A and the like are newly registered together with the newly input rule. This is shown in FIG.

That is, with the priority flag 5C of the current rule updated to OFF, the probability information 5A of the newly registered rule Rβk is set to NULL (reset state) and the frequency information 5B is set to "1". Then, the priority flag 5C is set to ON and the trust flag 5D is set to OFF.

These processes are repeatedly executed for all clauses for each clause.

As described above, according to this embodiment, when the rule is updated and learned, the information on whether the priority flag 5C is ON or OFF is effective from the next analysis processing, and A rule set {Rβ1, whose priority flag is on,
Rβ2, ...} will be applied in preference to {Rα1, Rα2, ...}. As described above, resetting the priority flag 5C to be on or off has a smaller calculation load than a case where the probability of the rule is changed every time. In addition, since the learning effect is immediately effective, a very easy-to-use natural language processing device can be obtained.

Further, since this natural language processing device stores the content of the reliability flag 5D in the rule storage unit 5, it becomes possible to consider it as an inspection target at the time of readjustment of the rule at a later date, and the accuracy of the rule. Can be further enhanced.

(3) Other Embodiments In the above embodiment, the case where the value of the frequency information 5B is increased or decreased by "1" in step SP7, step SP10, and step SP11 has been described. In order to prevent the value of the frequency information 5B from being counted up without meaning when repeatedly analyzing the same sentence, the increase / decrease may be determined according to whether or not the user can update the frequency information 5B. Is not limited to the case of increasing or decreasing by "1".

In the above embodiment, the update of the probability information 5A of each rule was not described, but the probability information 5A is set when the user gives an instruction for recalculation or is set in advance. When the predetermined number of times has been reached, the recalculation may be performed based on the frequency coasting report 5B. In this way, the burden of executing the calculation of the probability information 5A every time can be reduced.

Further, in the above-described embodiment, the rule storage 5 stores the probability information 5A, the frequency information 5B, and the priority flag 5.
4 pieces of information of C and trust flag 5D are stored, and these 4 pieces of information are stored.
The case where the input sentence is analyzed using one piece of information has been described, but the present invention can be applied to the case where information obtained by combining one or more of these pieces of information is stored and the input sentence is analyzed based on these pieces of information.

For example, 2 of probability information 5A and priority flag 5C
It can also be applied when storing only one information. Even in this way, if the priority flag 5C is turned on among a plurality of candidates, a new rule can be preferentially applied in the analysis of an input sentence having a similar structure, and a pattern that has failed once will be next time. Can be processed correctly.

Furthermore, in the above embodiment,
Although the learning of the rules used in the analysis process or the generation process has been described, the present invention is not necessarily limited to the learning of the rules and can be widely applied to the case of learning dictionary data.
For example, it can be similarly applied to a learning process for a case grammar of a verb. For example, in the dependency analysis of "I climbed a mountain to see the scenery.", Two case grammars for "Climb"("Noun (human, animal) ga" and "Noun (high place)")
Two case grammars for "seeing"("nouns (humans, creatures)"
It can also be applied when analyzing that a "mountain" relates to "climbing" from the relationship between "ga" and "noun (object)". At this time, the four case grammars for the verb can be regarded as the same as the rules in the above-described embodiment, and the frequency information 5A, the priority flag 5C, and the like can be applied as in the present embodiment.

[0051]

As described above, according to the present invention, with respect to each element constituting the natural language processing rule, two pieces of information, probability information and priority information, are associated with each other, and the processing result of the input language by the processing unit is When the result is incorrect, the priority information of the element that derives the result is rewritten to a lower priority, and when the processing result is correct, the priority information of the element that derives the result is rewritten to a higher priority. When an input sentence with the same structure as an input sentence that has had an error in is input, the element whose priority information has been rewritten with high priority in the previous process is selected preferentially in this process. Therefore, it is possible to realize a natural language processing device that can obtain a correct processing result without repeating the same error twice.

As described above, according to the present invention, two pieces of information, probability information and frequency information, are associated with each other for each element constituting the natural language processing rule, and the processing result of the processing unit is incorrect. , The value of the frequency information indicating the number of times the element that derives the result is adopted is decreased, and when the processing result of the processing unit is correct, the value of the frequency information indicating the number of times the element that derives the result is adopted is increased. By rewriting only the frequency information instead of rewriting the probability information each time one process is completed, the burden on the processing unit can be reduced as compared with the case where the probability information is recalculated and rewritten. Therefore, it is possible to realize the natural language processing device that can improve the processing speed.

Furthermore, as described above, according to the present invention,
Probability information and reliability information are associated with each of the elements constituting the natural language processing rule, and when the processing result of the processing unit is highly likely to be incorrect, the reliability corresponding to the element that derives the result. By lowering the reliability of the reliability information, it is possible to prevent elements with low reliability of reliability information from being used in the processing of the input language on and after the next time, and the natural language processing with high accuracy of the processing result. The device can be realized.

[Brief description of drawings]

FIG. 1 is a block diagram showing a natural language processing device according to an embodiment.

FIG. 2 is an operation flowchart executed by the natural language processing device according to the embodiment.

FIG. 3 is an explanatory diagram for explaining an analysis result of a dependency analysis and a correspondence rule set in the embodiment.

FIG. 4 is an explanatory diagram showing a state change when a rule to be updated exists in an analysis rule in the embodiment.

FIG. 5 is an explanatory diagram showing a state change when a rule to be updated does not exist in the analysis rule in the embodiment.

[Explanation of symbols]

1 ... Natural language processing device, 2 ... Input unit, 3 ... Processing unit, 4 ... Dictionary storage unit, 5 ... Rule storage unit, 6 ... Result storage unit, 7 ... Update learning unit.

Claims (5)

[Claims] 1. A storage device that stores two pieces of information, probability information and priority information, for each element that constitutes a natural language processing rule, and a processing unit that processes an input language using the element of the language resource. When the processing result of the processing unit is incorrect, the priority of the priority information of the element that derives the result is rewritten to a lower priority,
A natural language processing apparatus comprising: an update learning unit that rewrites the priority information of an element that derives the processing of the processing unit to a correct result to a higher priority. 2. The priority information is rewritten so that the former priority is lower when an appropriate element is present as compared with the element even if the processing result is not incorrect. Natural language processing device described in. 3. A storage device that stores two pieces of information, probability information and frequency information, for each element that constitutes the natural language processing rule, a processing unit that processes an input language using the element of the language resource, When the processing result of the processing unit is incorrect, the value of the frequency information indicating the number of times the element that derives the result is adopted is reduced, and when the processing result of the processing unit is correct, the element that derives the result is A natural language processing device, comprising: an update learning unit that increases the value of the frequency information that indicates the number of times that the natural language processing unit has been adopted. 4. The natural language according to claim 3, wherein the probability information is recalculated based on the value of the frequency information when the number of times of processing the input language reaches a predetermined number. Processing equipment. 5. A storage device that stores two pieces of information, probability information and reliability information, for each element that constitutes the natural language processing rule, and a processing unit that processes an input language using the element of the language resource. When it is highly possible that the processing result of the processing unit is incorrect,
A natural language processing device, comprising: an update learning unit that reduces the reliability of reliability information corresponding to an element that derives the result.