JP3379608B2 - Method of determining meaning similarity between words - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for determining semantic similarity between words, and more particularly to the similarity of meaning between words required in various natural language processing such as fuzzy search processing of database and machine translation. The present invention relates to a method for determining the meaning similarity between words for determining.

[0002]

2. Description of the Related Art With the development of workstations WS and other data processing devices, large-capacity databases whose stored contents are expressed in natural language rather than numerical values, such as document databases, are being widely used. With respect to this kind of large-capacity database, a set of words representing the contents of a document to be searched is input as a keyword, and the document itself or a document related thereto is searched. In such a database search process, the technology that is the core of the process is a technique that determines the similarity in meaning between compared words, and the performance of the search process is determined by the quality of this similarity determination technique. Will be. This applies not only to database search processing, but also to various natural language processing such as machine translation.

Basically, such a similarity determination of meanings between words is performed based on a word meaning database called a concept base in which meanings of a large number of words are stored in advance. There is. Referring to FIG. 1, in this concept base, for each word w _i , the word w
_An attribute v _j representing the feature of _i , its word w _i and its attribute v
It is common practice to prepare a plurality of pairs of importance a _ij indicating the depth of the relationship with _j . If this concept base is expressed in a table format, it becomes as shown in FIG. In FIG. 1, w
_{l to} w _n indicate each word, and v _{l to} v _m indicate each attribute.
The entire attributes v _{1 to} v _m are called an attribute set. The importance a _ij is
If the word w _i is not characterized by the attribute v _j , ie, the word w _i is not associated with the attribute v _j , then a _ij = 0. The importance a _ij is normalized by some method. For example, the square root of the sum of squares of importance = 1 is normalized.

In the case of determining the similarity of meaning between words using such a concept base, basically, a method of calculating a distance of some meaning between two words to be compared has been proposed. ing. For example, two words w _i and word w
_It is assumed that _j is represented by a vector having the attribute v _h as an element, and the similarity, which is a measure of similarity, is calculated by the inner product as in the following formula (for details, see the patent applicant). See the specification of Japanese Patent Application No. 4-315233 related to the application).

[0005] The above is the word w_l= "Horse", word w₂ = Taking "pig" as an example
I will explain. About "horse", v₂ = “Mane
(0.3) ”, v₃ = "Animal (0.8)", v₈ = "Tail
(0.7) ”can be used as the attribute.
"Pig" w₂ About v ₃ = "Animal (0.8)", v₇ 
= "Meat (0.5)", v₉ = “Tail (0.2)”
It can be the attribute. The number in () is 1
Shows the normalized importance to. The word "horse" and the word "pig"
Calculate the degree of similarity to determine the similarity between
Then, the common attribute is v₃ Focus on the importance of
And can be calculated as follows. Similarity = 0.8 × 0.8 = 0.64 As described above, when the similarity is calculated by the equation (1),
What is important to choose as a set of attributes in the mind-based
Becomes In the simplest case, the word is to be characterized.
However, it is a good idea to select this word as an attribute.
available. The attribute set is the same as the set of all words.
is there. In this case, if the word "horse" is taken as an example, as described above.
In that case, "mane," "tail," and other attributes can be used as its attributes.
it can. However, in the case of the method of selecting a word as an attribute,
Synonyms may exist in the attribute set,
Accurate similarity cannot be obtained depending on the calculation of equation (1).
It That is, the expressions (1) are semantically the same.
"Tail" and "tail" are treated as different attributes,
The importance of these attributes does not contribute to the similarity. "Horse" or
Since one of the attributes of "pig" is "0", these
The product between attributes is zero, and as a result, it is not added as a similarity.
It is.

For the above reason, in order to improve the accuracy of discrimination, for synonyms, for example, one word is represented as an attribute, or a thesaurus is used to represent a superordinate concept of a characteristic word. Is defined as an attribute to form an attribute set. In order to represent one word as an attribute, the synonyms of “tail” and “tail” are all represented by “tail”. When the thesaurus is adopted, if "thumb", "child finger" or other words representing the kind of finger is selected as the characteristic word, "finger" which is a superordinate concept thereof is represented as the attribute. That is,
In order to obtain high discrimination accuracy, the attribute set should ideally be constructed so that the attributes are semantically independent of each other and orthogonal to each other, and various innovations have been made for that reason. . However, no matter how you try, it is impossible to select attribute groups that are semantically independent of each other, as long as the ones that have meaning like words are selected as the attribute set. It's just one way to do it.
Even if "tail" is used as a representative of synonyms, it cannot be guaranteed that all other attributes are semantically independent of, or in other words dissimilar to, "tail". The same applies when a word representing a superordinate concept of a characteristic word is used as an attribute.

As described above, when the semantic similarity between words is determined by using the concept base, the determination is made by calculating the distance of some meaning between two words as shown in equation (1). There is an inherent factor in the method that reduces the accuracy of discrimination. It is possible to perform some conversion operation so that the attributes do not have any meaning to form an attribute set consisting of mutually independent attributes, but this is also considered to be extremely difficult in reality.

[0008]

SUMMARY OF THE INVENTION The present invention is an inter-word semantic similarity discrimination method for discriminating semantic similarity between words using the concept base as described above. Provided is a method of determining semantic similarity between words, which selects a word group as a group and obtains high discrimination accuracy even if these word groups are not semantically independent of each other.

[0009]

Meaning of a word is expressed by a set of a pair of an attribute v representing a feature of a word w and an importance a of the attribute indicating the depth of the relation between the word and the attribute. Of the meaning of each word using a word meaning database
In the inter- word semantic similarity determination method of calculating the distance to determine the similarity between words , the attribute v _{k is associated} with each of the two words w _i and w _j that are targets of the similarity determination.
And v _l , the importance a of the two selected attributes
A product of _ik and a _jl and a quantity L _kl representing the semantic closeness between these two attributes v _k and v _l a _ik × a _jl × L _kl
We constructed a method for determining the semantic similarity between words, where the similarity is the result of summing all the attributes included in two words.

Then, a method for discriminating semantic similarity between words is constructed in which the amount representing the semantic closeness between the two attributes is the reciprocal of the distance on the thesaurus between the two selected attributes. In addition, a method for determining the meaning similarity between words is configured in which the amount representing the semantic closeness between the two attributes is the information amount of the superordinate concept on the thesaurus common to the two selected attributes.

[0011]

Embodiments of the present invention will be described. This invention
The degree of similarity, which is an index for determining the semantic similarity between words, is not calculated by simply calculating the distance between two words as in the above-described conventional example, but two words w Attribute v _k and attribute v _l for _i and word w _j respectively
, The importance a _{ik of} the selected attribute v _k and the attribute v
and severity a _jl of _l, summing the three parties of the product of the amount L _kl representing the semantic proximity between these attributes v _k and attribute v _l, as the following expression (2) for the combination of all the attributes It is calculated by

[0012] L _kl is a quantity that represents the semantic closeness on the thesaurus between the attributes v _k and v _l .

As the quantity representing the semantic closeness, the reciprocal of the distance between the attributes on the thesaurus, or the information quantity of the superordinate concept on the thesaurus common to these attributes and other quantities can be applied. Then, the thesaurus is structured in a tree structure in which all words are semantically hierarchized. An example of such a thesaurus is shown in FIG. Here, the present invention considers that, no matter what word is selected as an attribute of the attribute set, this word is semantically the same as all other words in the superordinate concept of any level. Is. When one of the corresponding attributes of the two words has an importance of “0”, the corresponding attribute of the two words, that is, the attribute v in which h is the same, is calculated according to the equation (1). _If either importance a _ih or a _{jh of h} is “0”, the product of these, the similarity, is “0”, and the importance of the other attribute does not contribute to the similarity at all. However, the present invention, for all combinations of attributes of two words that determine the similarity of meanings, calculates the product of these importance levels and the quantity representing the semantic closeness between these importance levels. Since the similarity is calculated by calculating and summing these products, even if one of the corresponding attributes of two words has an importance of “0”, the importance is “ If the other attribute that is not "0" is semantically similar to any of the other uncorresponding attributes, the product between them is not "0".
This similarity will be added and reflected in the calculation of the degree of similarity in Expression (2).

In the calculation of the degree of similarity of the formula (2) according to the present invention, the quantity L _kl representing the semantic closeness is further multiplied, so that the more similar these two attributes are in terms of meaning, the better. The similarity will be largely reflected by the calculation of the similarity. That is, according to the present invention, since the similarity between all attributes is considered in the calculation of the similarity in the determination of the semantic similarity between words, the attribute set includes semantically similar attributes. Even if they are not independent, high similarity discrimination accuracy can be obtained.

The embodiment of the present invention will be described more specifically. Here, a case will be described in which the reciprocal of the distance on the thesaurus between these attributes is used as a quantity representing the semantic closeness between the attributes. In this case, the semantic similarity between words is calculated by using the reciprocal of the distance on the thesaurus as L in Expression (2).

Referring to FIG. 1, the two words to be compared are w _i and w _j , and the attribute sets are v ₁ to v _m.
Then, the word w _i and the word w _j are m-dimensional vectors having the importance (a _{i1 to} a _im , a _{j1 to} a _jm ) of the attributes v _{1 to} v _m as elements, and are represented as follows. It w _i = (a _i1 , a _i2 , ..., a _ik , ... a _im ) w _j = (a _j1 , a _j2 , ..., a _jl , ... a _jm ) where the word Needless to say, the importance level a _ih is “0” when the attribute v _h is not a characteristic of w _i . The same applies to the word w _j . Each attribute of the attribute set has a meaning, and here is a word itself. That is, the word w _i is the word v ₁
~ V _m as attributes, and the importance a _{i1 of} each of these attributes ~
Let it be characterized by a _im .

FIG. 3 shows an example of a thesaurus of word groups which are each attribute of the attribute set. That is, the thesaurus has a tree structure, the word v ₁ is the attribute belongs to the preamble J _11, the preamble J ₁₁ itself is further belong to the broader concept J _21. It does not matter if the superordinate concept is the word itself. Assuming such a thesaurus, there is always a distance between any attributes, and the closer the distance is, the closer the attributes can be to each other. Here, the distance between both attributes is the number of branches from one attribute to the other attribute. In FIG. 3, for example, the distance between the attribute v _k and the attribute v _l is “6”.

In this embodiment, the similarity is (2)
It will be calculated as the sum of the product of the importance of the two attributes selected for each word and the reciprocal of the distance on the thesaurus between these attributes, based on the formula. That is, the similarity is obtained by multiplying the product of the vector elements in the word w _i and the word w _j by the reciprocal of the distance between the corresponding attributes and summing these products. In addition,
The reciprocal of the distance between the corresponding attributes is 1/6 in the previous example.
Is. Therefore, between two words to be compared, the similarity is high when the attributes whose importance is not “0” are close to each other on the thesaurus, and the similarity is high when they are far from each other. Get lower. It goes without saying that this is true even when there is no attribute whose importance is non-zero for both words. In this case, according to the calculation of the conventional example by the formula (1), the sum of products of the importance of common attributes is used as the similarity, and thus the similarity is always “0”.

Next, a method of using the information amount of the superordinate concept common to these attributes on the thesaurus as the amount representing the semantic closeness between the attributes will be described as another embodiment.
Only the amount representing this semantic closeness is different from the previous embodiment, and the others are exactly the same, so only the method for obtaining this information amount will be described. The information amount l of this superordinate concept is defined as follows.

L = log ₂ (A / n) (3) Here, A: the total number of words in which the importance of any one of the attributes located in the lower order of the common superordinate concept is non-zero n: Concept-Based Total Word Count With reference to FIG. 3, consider the relationship between attributes v ₂ and v ₄ . The common superordinate concept of these attributes is J ₂₁ . Here, A is the total number of words in which any of the attributes v _{1 to} v ₄ has a non-zero importance, the concept base is as shown in FIG. 4, and the circles in the figure indicate the importance. Assuming it is non-zero, the corresponding words are w ₁ , w ₂ ,
There are a total of 6 of w ₃ , w ₅ , w ₆ , and w ₈ , and A = 6. When the information amount is defined in this way, when two attributes are close to each other on the thesaurus, the information amount of the superordinate concept common to them is large, and when they are far apart, the information amount is small. That is, this information amount equivalently represents the semantic closeness between the attributes.

As described above, the reciprocal of the distance between the attributes on the thesaurus is defined as the quantity representing the semantic closeness between the attributes.
Or, the example of selecting the information amount of the superordinate concept common to these attributes on the thesaurus was explained, but any amount similar to these examples can be selected as long as it is the amount indicating the semantic closeness between attributes. It doesn't matter.

[0022]

As described above, according to the present invention, when determining the semantic similarity between words, the significance levels of all the attribute combinations of two words to be compared are calculated. The similarity is calculated by calculating the product of three parties of the quantities that represent the semantic closeness between these degrees of importance, and summing these products to calculate the similarity. Even if the attributes to be included are not independent of each other,
High similarity discrimination accuracy can be obtained. Furthermore, depending on the conventional calculation method, for words with few non-zero importance attributes that characterize, the similarity determination accuracy may decrease because there are few common attributes. In the present invention, since the similarity is calculated in consideration of all the attribute combinations, such a drawback is eliminated.

[Brief description of drawings]

FIG. 1 is a diagram showing a concept base.

FIG. 2 is a diagram showing a structure of a thesaurus.

FIG. 3 is a diagram illustrating a semantic distance between attributes.

FIG. 4 is a diagram illustrating a method of calculating an information amount of a common superordinate concept.

[Explanation of symbols]

w _i word w _j word v _k attribute v _l attribute a _ik importance degree a _jl importance degree L _kl quantity representing the semantic closeness between two attributes

Continuation of front page (56) References JP-A-6-162099 (JP, A) JP-A-6-208590 (JP, A) JP-A-6-131184 (JP, A) Yuji Sawada, Takenao Okawa, Babaguchi Noboru, Realization of Association Mechanism Considering Point of View, Journal of Information Processing Society, May 15, 1994, Volume 35, No. 5, pp. 714-724 Takayuki Sakano, Junichi Nakamura, Masashi Yoshida, Generating Metaphorical Expressions Utilizing Appearance Similarity and Psychological Evaluation between Concepts, IEICE Technical Report, July 8, 1993, 93rd Vol. 131, pp. 17-24 Kasahara Kaname, Matsuzawa Kazumitsu, Ishikawa Tsutomu, Kawaoka Tsukasa, Similarity Discrimination for the year based on viewpoint, Journal of Information Processing, March 15, 1994, Vol. 35, No. 3, pp. 505-509 (58) Fields investigated (Int.Cl. ⁷ , DB name) G06F 17/30 G06F 17/27 JISST file (JOIS)

Claims (3)

(57) [Claims] 1. A word meaning database is used which expresses the meaning of a word by a set of a pair of an attribute representing a feature of a word and a degree of importance of the attribute indicating a depth of association between the word and the attribute. Then, the distance between the words is calculated and the word phase is calculated.
In a method for determining a meaning between words, which determines similarity between words, an attribute is selected for each of the two words that are the objects of similarity determination, and the importance of the two selected attributes and the Inter-word semantic similarity, characterized in that the similarity is the result of summing the products of three parties representing the amount of semantic closeness between two attributes for all combinations of attributes included in two words. Gender discrimination method. 2. The inter-word semantic similarity determination method according to claim 1, wherein an amount representing a semantic closeness between two attributes is a distance between two selected attributes on a thesaurus. A method for discriminating semantic similarity between words, which is a reciprocal of. 3. The inter-word semantic similarity determination method according to claim 1, wherein an amount representing a semantic closeness between two attributes is set on a thesaurus common to the two selected attributes. A method for determining semantic similarity between words, which is characterized in that the information amount of a superordinate concept is used.