JP2005149014A - Method, device and program for obtaining document related word - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain related words even from a document related with a plurality of fields or a document with some errors such as voice recognition results. <P>SOLUTION: An input document is divided into words and a conceptual base is retrieved to obtain vectors corresponding to all words obtained. Based on the vectors obtained, the words are clustered to form a plurality of clusters. Based on the clusters, document field vectors showing the fields to which the document belongs are determined. Based on the document field vectors and word field vectors showing the fields of all words in a word database, degrees of word associations showing the association between the input document and each word are determined. Based on the degrees of word associations obtained, the words are ranked and related words are outputted according to the rank about the words. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a document related vocabulary acquisition method, apparatus, and program, and more particularly, to a document related vocabulary acquisition method, apparatus, and program for acquiring related vocabulary from a document.

  Research has been conducted on how to acquire a related document from a document and how to acquire a related vocabulary from a set of words, but how to acquire a related vocabulary from a document. Not much research has been done.

As a method of acquiring a vocabulary related to the document from the document, a method of extracting a related word included in the document for the purpose of constructing a thesaurus (for example, see Patent Document 1), or a purpose of searching for a document related to a certain document There is a method for extracting related keywords in the document (see, for example, Patent Document 2).
JP 2003-256447 A JP-A-11-025108

  However, it is assumed that the related words and related keywords obtained by the above method are always included in the original document, and the vocabulary is only extracted by searching the document. Vocabulary that does not appear in the document cannot be acquired. Therefore, for example, even if a vocabulary can be extracted from a document for the purpose of searching for a document related to a certain document, the related document cannot be searched if the extracted vocabulary is not included in the related document.

  In addition, it is possible to divide a document into words, acquire a set of words, and acquire the related vocabulary from the set of words, but when applying a method to acquire the related vocabulary from the set of words In addition to the premise that the set of words is given in advance a correct keyword of a specific field, the vocabulary that can be acquired is also premised on the specific field, whereas the document The set of words that can be divided into words includes words that are not related to the field, and if the original document contains errors, the set of words also contains errors, Therefore, it is impossible to apply a method of acquiring a related vocabulary by directly inputting a set of words obtained by dividing a document into words.

  The present invention has been made in view of the above points, and even if a document is related to a plurality of fields or a document that includes some errors such as a speech recognition result, a vocabulary related to the document. It is another object of the present invention to provide a document-related vocabulary acquisition method, apparatus, and program that can acquire vocabulary, and that includes the acquired vocabulary that does not appear in the original document.

  FIG. 1 is a diagram for explaining the principle of the present invention.

A document related vocabulary acquisition method for acquiring a vocabulary related to a document from a document,
A word dividing step (step 1) for dividing the input document into words;
A word vector obtaining step (step 2) for obtaining a vector corresponding to each word obtained in the word dividing step by searching a concept base in which a vector expressing the meaning of the word is stored;
A word clustering step (step 3) for performing clustering of words based on each vector obtained in the word vector acquisition step and creating a plurality of clusters;
A document field vector calculation step (step 4) for obtaining a document field vector representing the field of the document based on each cluster obtained in the word clustering step;
The relationship between the input document and each vocabulary is shown from the document field vector obtained in the document field vector calculation step and the vocabulary field vector representing the vocabulary field in the vocabulary database created in advance. A vocabulary relevance calculating step for calculating a vocabulary relevance (step 5);
A vocabulary ranking step (step 6) for ranking vocabulary based on the relevance obtained in the vocabulary relevance calculation step;
A related vocabulary output step (step 7) for outputting related vocabulary based on the vocabulary rank obtained in the vocabulary ordering step is performed.

In the document field vector calculation step (step 4), the present invention selects the top Q clusters in the order in which many words are included,
For each selected cluster, the document field vector is obtained for each cluster by taking the centroid of the word vector of each word included in the cluster.

In the document field vector calculation step (step 4), the present invention
A document field vector is obtained using the reliability of the speech recognition result of each word as a weight.

  FIG. 2 is a principle configuration diagram of the present invention.

The present invention is an inter-document collocation acquisition device for acquiring a vocabulary related to a document from a document,
A concept base 8 in which vectors representing the meaning of words are stored;
Word dividing means 1 for dividing the input document into words;
Word vector acquisition means 2 for acquiring a vector corresponding to each word obtained by the word division means 1 by searching the concept base 8;
Word clustering means 3 for clustering words based on each vector obtained by the word vector acquisition means 2 to create a plurality of clusters;
A document field vector calculating unit 4 for obtaining a document field vector representing a field of the document based on each cluster obtained by the word clustering unit 3;
The relationship between the input document and each vocabulary from the document field vector obtained by the document field vector calculating means 4 and the vocabulary field vector representing the field of each vocabulary in the vocabulary database 9 created in advance. Vocabulary relevance calculating means 5 for obtaining a lexical relevance indicating gender,
Vocabulary ranking means 6 for ranking the vocabulary based on the degree of association obtained by the vocabulary relation degree calculation means 5;
And a related vocabulary output means 7 for outputting a related vocabulary based on the vocabulary ranking obtained by the vocabulary ranking means 6.

The document field vector calculation means 4 of the present invention
Means for obtaining a document field vector for each cluster by selecting the top Q clusters in the order in which many words are included and taking the centroid of the word vector of each word included in the selected cluster. Have

The document field vector calculation means 4 of the present invention
Means for obtaining a document field vector with the reliability of the speech recognition result of each word as a weight.

The present invention is a document related word acquisition program for causing a computer to acquire a vocabulary related to a document from a document,
A word splitting step for splitting the input document into words;
A word vector acquisition step of acquiring a vector corresponding to each word obtained in the word division step by searching a concept base in which a vector expressing the meaning of the word is stored;
A word clustering step of clustering words based on each vector obtained in the word vector acquisition step to create a plurality of clusters;
A document field vector calculation step for obtaining a document field vector representing the field of the document based on each cluster obtained in the word clustering step;
The relationship between the input document and each vocabulary is shown from the document field vector obtained in the document field vector calculation step and the vocabulary field vector representing the vocabulary field in the vocabulary database created in advance. A vocabulary relevance calculating step for obtaining a vocabulary relevance;
A vocabulary ranking step of ranking the vocabulary based on the relevance obtained in the vocabulary relevance calculation step;
The computer executes a related vocabulary output step of outputting a related vocabulary based on the vocabulary order obtained in the vocabulary ordering step.

In the document field vector calculation step, the present invention selects the top Q clusters in the order in which many words are included;
The computer is caused to execute a step of obtaining a document field vector for each cluster by taking the centroid of the word vector of each word included in the cluster for each selected cluster.

Further, the present invention provides a document field vector calculation step,
The computer is caused to execute a step of obtaining a document field vector using the reliability of the speech recognition result of each word as a weight.

  In the present invention, words obtained from an input document are clustered to create a cluster for each field, and the field to which the document belongs is estimated from the cluster. Therefore, an error is included in the word obtained from the document. Even if there are words that are not related to the field, those words are ignored and the field can be estimated correctly, and as a result, an appropriate related vocabulary can be obtained.

  Even when a document belongs to a plurality of fields, a plurality of fields can be estimated from a plurality of clusters. Furthermore, since a large amount of vocabulary is prepared in advance and vocabulary close to the field of the document is acquired from the vocabulary, vocabulary that is not included in the document can be acquired. The vocabulary acquired in this way can be used for searching related documents in document search.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 3 shows a configuration of a document-related vocabulary acquisition apparatus according to an embodiment of the present invention.

  The related document vocabulary acquisition apparatus shown in FIG. 1 includes a word division unit 1, a word vector acquisition unit 2, a word clustering unit 3, a document field vector calculation unit 4, a vocabulary relevance calculation unit 5, a vocabulary ranking unit 6, and a related vocabulary output. The unit 7, the concept base 8, and the vocabulary database 9 are configured.

  The word dividing unit 1 divides the input document into words.

  The word vector acquisition unit 2 acquires a vector corresponding to each word obtained by the word division unit 1 by searching the concept base 8 in which a vector expressing the meaning of the word is stored.

  The word clustering unit 3 performs word clustering based on each vector obtained by the word vector acquisition unit 2 and creates a plurality of clusters.

  The document field vector calculation unit 4 obtains a document field vector representing the field of the document based on each cluster obtained by the word clustering unit 3.

  The vocabulary relevance calculating unit 5 receives the document input from the document field vector obtained by the document field vector calculating unit 4 and the vocabulary field vector representing each vocabulary field in the vocabulary database 9 created in advance. The degree of vocabulary relevance indicating the relationship between each vocabulary is obtained.

  The vocabulary ranking unit 6 ranks the vocabulary based on the relevance obtained by the vocabulary relevance calculation unit 5.

  The related vocabulary output unit 7 outputs the related vocabulary based on the rank related to the vocabulary obtained by the vocabulary ordering unit 6.

  4 to 8 show flowcharts of processing of the document-related vocabulary acquisition apparatus in one embodiment of the present invention.

  FIG. 4 is a flowchart showing overall processing in one embodiment of the present invention.

  First, the word dividing unit 1 divides a document into words (step 100). Next, the word vector acquisition part 2 acquires the word vector of each word (step 200).

  FIG. 5 is a flowchart of the processing of the word vector acquisition unit in one embodiment of the present invention. In the figure, N is the number of words obtained by the word dividing unit 1, and W represents the number of words existing in the concept base 8.

  The word vector acquisition unit 2 checks for each word obtained by the word dividing unit 1 whether or not the word exists in the concept base 8 (step 220). If there is, the word vector is obtained from the concept base 8. Obtain (Step 230, Yes, Step 250). If it does not exist (step 230, No), the word is deleted (step 240). This process is performed for all words (steps 260, 270, 280).

  Next, word clustering is performed by the word clustering unit 3 (step 300).

FIG. 6 is a flowchart of the processing of the word clustering unit in one embodiment of the present invention. Here, C _I is the I-th cluster, I is the number of clusters, P is represents a threshold value of the number of clusters to be used as the termination condition of the clustering.

  The word clustering unit 3 creates a cluster of one word for each of the W words obtained by the word vector acquisition unit 2 (step 310). Among these W clusters, two clusters having the closest distance are obtained (step 330), and these two clusters are merged into one to create W-1 clusters (steps 340 and 350). The process of merging the two clusters into one in this way is repeated until the number of clusters becomes P or less (step 360).

  Next, the document field vector calculation unit 4 calculates the document field vector (step 400).

  FIG. 7 is a flowchart of processing of the document field vector calculation unit according to the embodiment of the present invention. Here, I represents a number when clusters are arranged in descending order of the number of words, and Q represents the number of document field vectors to be acquired.

  The document field vector calculation unit 4 obtains the center of gravity of the cluster having the largest number of words and sets it as the I-th document field vector (step 420). This process is repeated Q times (steps 430 and 440).

  Next, the vocabulary relevance calculator 5 calculates the vocabulary relevance (step 500).

  FIG. 8 is a flowchart of processing of the vocabulary relevance calculating unit in the embodiment of the present invention.

  The vocabulary relevance calculating unit 5 calculates the distance between the vocabulary field vector of the vocabulary and the Q document field vectors obtained by the document field vector calculating unit 4 for all vocabularies in the vocabulary database 9, and the closest distance is obtained. Is the vocabulary relevance level (steps 520 and 530). This process is repeated for all vocabularies.

  Next, the vocabulary ranking unit 6 sorts the vocabulary in descending order of the degree of vocabulary relevance and assigns ranks to the vocabulary (step 600). Lastly, the related vocabulary output unit 7 outputs V vocabulary items in descending order of lexical relevance (step 700).

  Hereinafter, the present embodiment will be described specifically.

  In order to show that an appropriate document-related vocabulary can be acquired even if there is an error in the input, the document shown in FIG. 10 which is the result of speech recognition of the speech read out from the document shown in FIG. .

  First, the word dividing unit 1 divides the document word. The result of the division is shown in FIG.

  Next, the word vector acquisition unit 2 acquires a word vector of each word from the concept base 8. Table 1 shows a concept-based example.

概念ベース８は、ハードディスク等の記憶手段に格納され、単語毎に、ｆ次元の単語ベクトルが付与されている。概念ベース８中の単語は、名詞や動詞、形容詞等の自立語である。概念ベース８における単語ベクトルは、意味的に類似している単語間ほど距離が近く、意味的に類似していない単語間程距離が遠くなるように値が設定されている。概念ベース８の例としては、例えば、特開平６−１０３３１５の『類似性判別装置』や、特開平７−３０２２６５の『類似性判別用データ精錬方法及びこの方法を実施する装置』で開示されているデータベースがある。

The concept base 8 is stored in a storage means such as a hard disk, and an f-dimensional word vector is assigned to each word. The words in the concept base 8 are independent words such as nouns, verbs, and adjectives. The word vectors in the concept base 8 are set such that the distance between words that are semantically similar is closer, and the distance between words that are not semantically similar is longer. Examples of the concept base 8 are disclosed in, for example, “Similarity Discriminating Device” of Japanese Patent Laid-Open No. 6-103315 and “Similarity Discriminating Data Refinement Method and Device for Implementing this Method” of Japanese Patent Laid-Open No. 7-302265. There is a database.

  Among words shown in FIG. 11, a word vector is assigned to a word that exists in the concept base 8, and a word that does not exist is deleted. As a result of the word vector acquisition processing, word vectors were assigned to the 25 words listed in the word column of Table 2, and the remaining words were deleted. Here, in this embodiment, “department” appearing in two places, “network department” and “sales department”, are treated as different words, but these may be combined into one as the same word.

次に、単語クラスタリング部３が、単語のクラスタリングを行う。単語ベクトルが付与された２５個の単語について、それぞれ１単語からなるクラスタを作成する。その結果を表２に示す。

Next, the word clustering unit 3 performs word clustering. For 25 words to which a word vector is assigned, a cluster consisting of one word is created. The results are shown in Table 2.

  Of these 25 clusters, two clusters having the shortest distance are obtained, and these two clusters are merged into one to create 24 clusters. Here, any distance scale may be used as long as it represents the similarity between the clusters, but here, the cosine distance between the centroids of the word vectors of the words included in the cluster is used as the distance scale. Use. “NTT”, “Service”, and “Department” each appear twice, but since the word vectors are the same for the same word, the distance between clusters having the same word is 1 and the most. Get closer. Here, the 6th and 14th clusters having the word “NTT” were merged. The results are shown in Table 3. Further, out of these 24 clusters, two clusters having the shortest distance are obtained, and these two clusters are merged into one to create 23 clusters. Such processing is repeated until the number of clusters satisfies the end condition.

本実施の形態では、クラスタ数が初期状態のクラスタ数である２５の半分より小さくなったとき、つまり、この場合はクラスタ数が１２個になったとき終了条件としたが、これに限るものではなく、固定的に１０個を終了条件としたり、初期状態のクラスタ数の３０％を終了条件としてもよい。クラスタ数が１２個となり、クラスタリングが終了したときのクラスタの状態を表４に示す。

In this embodiment, the termination condition is set when the number of clusters is smaller than half of the initial number of clusters of 25, that is, in this case, when the number of clusters becomes 12, but this is not a limitation. Alternatively, 10 may be fixed as end conditions, or 30% of the number of clusters in the initial state may be set as end conditions. Table 4 shows the cluster state when the number of clusters is 12 and clustering is completed.

次に、文書分野ベクトル算出部４が文書分野ベクトルの算出を行う。本実施の形態では、取得する文書分野ベクトルの数を１とするが、文書が複数の分野を含むような場合には、ここで文書分野ベクトルを複数求めればよい。表４より、最も単語数が多いクラスタは１番目のクラスタであるから、このクラスタから文書分野ベクトルを求める。

Next, the document field vector calculation unit 4 calculates the document field vector. In the present embodiment, the number of document field vectors to be acquired is 1. However, when a document includes a plurality of fields, a plurality of document field vectors may be obtained here. From Table 4, since the cluster with the largest number of words is the first cluster, the document field vector is obtained from this cluster.

  The center of the word vector of each word included in the cluster is set as the document field vector. In the present embodiment, when the document field vector is obtained, the weight of the word vector of each word is equal. However, when the input document is a speech recognition result, for each word in the document, Since the reliability indicating the degree of whether or not the word is correctly recognized is given, the center of the word vector weighted by the reliability of the word in the cluster may be used as the document field vector. By using reliability as a weight, even if a word with a recognition error is included in the cluster, the word with a recognition error generally has a low reliability, so the weight is small and the influence on the document field vector is also reduced. Get smaller. Therefore, the adverse effect of the recognition error word on the document field vector is reduced, and the field can be estimated correctly.

  Next, the vocabulary relevance calculation unit 5 calculates the vocabulary relevance from the vocabulary database 9. The vocabulary database 9 is a kind of concept base, in which vocabulary field vectors are set so that the distance between lexical words that are semantically similar is closer, and the distance between lexical words that are not semantically similar is longer. It is. The vocabulary database 9 is consistent with the concept base 8 used in the word vector acquisition unit 2, that is, the vocabulary field vector of the vocabulary in the vocabulary database 9 and the word vector of the words in the concept base 8 As long as they represent the same meaning, any may be used, and the concept base 8 itself used in the word vector acquisition unit 2 may be used, or may be created using the concept base 8. Here, the vocabulary field vector of a certain vocabulary is obtained from the concept base 8 of the word vector of the word that appears in the sentence with respect to a sentence in which the vocabulary appears in a document such as a large number of newspaper articles, and the centroid of the word vector is obtained. The vocabulary database 9 is used in which the vocabulary field vector for each sentence is used and the centroid of the vocabulary field vector for each sentence is obtained as the vocabulary field vector. For all vocabularies in the vocabulary database 9, the distance between the vocabulary field vector of the vocabulary and the document field vector obtained by the document field vector calculation unit 4 is obtained, and the closest distance is set as the vocabulary relevance. In the present embodiment, since there is one document field vector, the distance between the vocabulary field vector and the document field vector may be simply set as the vocabulary relevance level. Table 5 shows the result of the lexical relevance.

次に、語彙順位付け部６により、語彙を語彙関連度の大きい順にソートし、語彙に順位を付与する。

Next, the vocabulary ranking unit 6 sorts the vocabulary in descending order of the degree of vocabulary relevance, and assigns a rank to the vocabulary.

  Finally, the related word vocabulary output unit 7 outputs a word having a high degree of vocabulary relevance as a related vocabulary. Here, the top 20 vocabularies were acquired as related vocabularies.

  In the present embodiment, the higher word is acquired as a related vocabulary as it is according to the ranking. However, the higher vocabulary may be acquired after removing the known vocabulary. Table 6 shows the acquired related vocabulary. Thereby, it is understood that the vocabulary related to the document is acquired.

また、上記で示した一連の動作をプログラムとして構築し、文書間連語語彙獲得装置として利用されるコンピュータにインストールし、ＣＰＵ等の制御手段により実行させる、または、ネットワークを介して流通させることも可能である。

It is also possible to construct the above-described series of operations as a program, install it on a computer used as an inter-document continuous vocabulary acquisition device, execute it by a control means such as a CPU, or distribute it via a network. It is.

  Further, the constructed program is stored in a hard disk device connected to a computer used as a document-related vocabulary acquisition device, a portable storage medium such as a flexible disk, a CD-ROM, etc., and installed and executed on the computer. It is also possible.

  The present invention is not limited to the above-described embodiment, and various modifications and applications can be made within the scope of the claims.

  The present invention can be applied to retrieval of related documents.

It is a figure for demonstrating the principle of this invention. It is a principle block diagram of this invention. It is a block diagram of the document related vocabulary acquisition apparatus in one embodiment of this invention. It is a flowchart which shows the whole process in one embodiment of this invention. It is a flowchart of the process of the word vector acquisition part in one embodiment of this invention. It is a flowchart of the process of the word clustering part in one embodiment of this invention. It is a flowchart of a process of the document field vector calculation part in one embodiment of this invention. It is a flowchart of the process of the vocabulary related degree calculation part in one embodiment of this invention. It is an example of the document which does not contain the voice recognition error used as input in one embodiment of the present invention. It is an example of the document containing the speech recognition error in one embodiment of this invention. It is a figure which shows the result of having performed the word division | segmentation in one embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Word division means, Word division part 2 Word vector acquisition means, Word vector acquisition part 3 Word clustering means, Word clustering part 4 Document field vector calculation means, Document field vector calculation part 5 Vocabulary relevance calculation means, vocabulary relevance calculation part 6 Vocabulary ranking means, vocabulary ranking section 7 Related vocabulary output means, related vocabulary output section 8 Concept base 9 Vocabulary database

Claims (9)

A document related vocabulary acquisition method for acquiring a vocabulary related to a document from a document,
A word splitting step for splitting the input document into words;
A word vector acquisition step of acquiring a vector corresponding to each word obtained in the word division step by searching a concept base in which a vector expressing the meaning of the word is stored;
A word clustering step of performing clustering of words based on each vector obtained in the word vector acquisition step and creating a plurality of clusters,
A document field vector calculation step for obtaining a document field vector representing a field of the document based on each cluster obtained in the word clustering step;
The relationship between the input document and each vocabulary from the document field vector obtained in the document field vector calculation step and the vocabulary field vector representing the field of each vocabulary in the vocabulary database created in advance. A lexical relevance calculating step for obtaining a lexical relevance indicating
A vocabulary ranking step of ranking vocabularies based on the relevance obtained in the vocabulary relevance calculation step;
A document-related vocabulary acquisition method comprising a related vocabulary output step of outputting a related vocabulary based on the ranking of the vocabulary obtained in the vocabulary ordering step. In the document field vector calculation step, the top Q clusters are selected in the order in which many words are included,
2. The document related vocabulary acquisition method according to claim 1, wherein for each selected cluster, a document field vector is obtained for each cluster by taking the centroid of the word vector of each word included in the cluster. In the document field vector calculation step,
The document related vocabulary acquisition method according to claim 1 or 2, wherein a document field vector is obtained by using the reliability of the speech recognition result of each word as a weight. An inter-document collocation acquisition device for acquiring a vocabulary related to a document from a document,
A concept base that stores vectors representing the meaning of words;
Word dividing means for dividing the input document into words;
Word vector obtaining means for obtaining a vector corresponding to each word obtained by the word dividing means by searching the concept base;
Word clustering means for performing clustering of words based on each vector obtained by the word vector acquisition means and creating a plurality of clusters;
Document field vector calculation means for obtaining a document field vector representing the field of the document based on each cluster obtained by the word clustering means;
The relationship between the input document and each vocabulary from the document field vector obtained by the document field vector calculating means and the vocabulary field vector representing the vocabulary field in the vocabulary database prepared in advance. A lexical relevance calculating means for obtaining a lexical relevance indicating
Vocabulary ranking means for ranking vocabulary based on the relevance obtained by the vocabulary relevance calculation means;
A document-related vocabulary acquisition device comprising: related vocabulary output means for outputting related vocabulary based on the vocabulary ranking obtained by the vocabulary ranking means. The document field vector calculating means includes:
Means for obtaining a document field vector for each cluster by selecting the top Q clusters in the order in which many words are included and taking the centroid of the word vector of each word included in the selected cluster. 5. The document-related vocabulary acquisition apparatus according to claim 4. The document field vector calculating means includes:
6. The document related vocabulary acquisition apparatus according to claim 4 or 5, further comprising means for obtaining a document field vector using the reliability of the speech recognition result of each word as a weight. A document related word acquisition program for causing a computer to acquire a vocabulary related to a document from a document,
A word splitting step for splitting the input document into words;
A word vector acquisition step of acquiring a vector corresponding to each word obtained in the word division step by searching a concept base in which a vector expressing the meaning of the word is stored;
A word clustering step of performing clustering of words based on each vector obtained in the word vector acquisition step and creating a plurality of clusters,
A document field vector calculation step for obtaining a document field vector representing a field of the document based on each cluster obtained in the word clustering step;
The relationship between the input document and each vocabulary from the document field vector obtained in the document field vector calculation step and the vocabulary field vector representing the field of each vocabulary in the vocabulary database created in advance. A lexical relevance calculating step for obtaining a lexical relevance indicating
A vocabulary ranking step of ranking vocabularies based on the relevance obtained in the vocabulary relevance calculation step;
A document-related vocabulary acquisition program that causes a computer to execute a related vocabulary output step of outputting a related vocabulary based on a rank related to the vocabulary obtained in the vocabulary ordering step. In the document field vector calculating step, selecting the top Q clusters in the order in which many words are included;
8. The document-related vocabulary acquisition program according to claim 7, wherein the computer executes a step of obtaining a document field vector for each cluster by taking the centroid of the word vector of each word included in the cluster for each selected cluster. In the document field vector calculation step,
9. The document related vocabulary acquisition program according to claim 7 or 8, wherein the computer executes a step of obtaining a document field vector by using the reliability of the speech recognition result of each word as a weight.

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