JP2015170062A - Document set analyzer, document set analysis method, and document set analysis program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To be able to analyze a document set by considering a relation between documents and a user's point of view.SOLUTION: Respective parts 10, 20, 30 specify a document set, evaluate a relation between respective documents in the document set, and determines whether the relation is a predetermined standard or not. A score calculation part 50 calculates a score of each document on the basis of a relation between each document and another document and a relation between each document and a user's point-of-view document specified by a user's point-of-view document extraction part 40. Respective parts 60 to 63 use a relation between documents and a score of each document to specify, from the whole of the document set, a topic included in the document set, a set of documents relating to the topic, and a role of each of the documents in the document set. An information output part 70 outputs, on the basis of information obtained by the parts 30, 50, 63, a list of main topics included in the document set and documents indicating the topics.

Description

  The present invention relates to a technique for analyzing a collection of a large number of electronic documents (hereinafter referred to as documents) that are present inside a computer or accessible via a computer network.

  Currently, there are a large number of documents including text such as Web pages, blog articles, and news articles. A user of a computer may have a request for “a major topic existing in a document collection” or “a user wants to access a document group related to a topic in the document collection” for this large volume document.

  As a method for realizing this, the method of Non-Patent Document 1 and the method of Patent Document 1 are known. Non-Patent Document 1 proposes a method of clustering documents and analyzing them, expressing each document as a word vector, and using similarities between vectors (such as cosine similarity) to integrate similar vectors Thus, documents related to similar topics are identified as clusters, and each cluster is regarded as a set of information related to a specific topic.

  Patent Document 1 proposes a method for analyzing topics in a document, expressing a relationship between a set of documents based on the similarity between documents in a graph structure, and paying attention to the degree of concentration of the relationship in the graph structure. Identify the documents that make up the main topic in the set. By using this analysis method, you can not only identify major topics and collect documents related to them, but also documents that show the central contents of topics, documents that are related to topics but contain unique contents, etc. Can be specified separately.

JP2008-059442

D. Cutting, D. Karger, J. Pedersen, and J. Tukry, `` Scatter / Gather: a cluster-based approach to browsing large document collections, '' Proc. Of SIGIR'92, (1992) H. Tong and C. Faloutsos: “Center-piece subgraphs: problem de≡nition and fast solutions”, Proc. Of KDD’06, (2006) Haveliwala, T. H .: Topic-sensitive PageRank, Proc. Of WWW '02, (2002) Konstas, I., Stathopoulos, V. and Jose, J. M .: On social networks and collaborative recommendation, Proc. Of SIGIR '09, (2009) Takeshi Yamada, Kazumi Saito, and Naonori Ueda. "Cross-Entropy Directed Embedding of Network Data," Proc. Of the 20th International Conference on Machine Learning (ICML2003), pp.832--839, 2003.

  However, the conventional techniques (Non-Patent Document 1, Patent Document 1, etc.) have the following problems.

  (1) That is, many document clustering methods such as Non-Patent Document 1 are based on the premise that all documents belong to one of the clusters. In particular, when considering analysis of social media, there are many documents that do not have much relation to other documents, that is, so-called “others”. Therefore, appropriate clustering cannot always be performed, and even if an attempt is made to identify a topic, there is a possibility that information that is truly meaningful cannot be acquired due to a lot of noise.

  (2) On the other hand, according to the method of analyzing the degree of concentration of the relationship between documents using the graph structure of Patent Document 1 or the like, the influence of the aforementioned “other” document can be omitted. In addition to simply dividing a document set into clusters, it is also possible to analyze the positioning of each document, such as a document with central contents or a document presenting related information in the cluster.

  However, since the method of Patent Document 1 performs analysis using only the relationship between documents, there is a need to analyze mainly from a viewpoint of a user who analyzes, for example, a topic that has become news even if a topic in the world is analyzed. , There is a possibility that it is not possible to consider a request to analyze mainly on contents related to a specific topic.

  (3) The present invention is made to solve the above-mentioned problems of the prior art, and in analyzing a document set, the analysis of the document set is performed in consideration of the user's analysis viewpoint while considering the relationship between documents. Making it possible is a solution issue.

  Therefore, the present invention evaluates the relationship between documents based on the document set, and expresses the relationship in a graph structure. The main topics are analyzed from the relationship between documents expressed in this graph structure. At that time, it is possible to identify a point of interest in the graph structure based on the viewpoint information given by the user, evaluate the relationship with the point of interest together, and analyze the topic in the document set according to the user's viewpoint To do. In particular, if a document set is regarded as a three-dimensional graph structure and the relationship between documents related to a specific topic is analyzed, a document that is the center of a topic, a document that gives related information, and the like can be specified.

  The document set analysis apparatus according to the present invention includes a document set specifying unit that specifies a document set based on a user request or a predetermined condition, and a relationship between documents included in the document set specified by the document set specifying unit. A relationship evaluation means for evaluating the nature, a relation evaluated by the relation evaluation means, a relation extraction means for identifying the presence / absence of a relationship between documents based on a predetermined criterion, and a viewpoint specified by a user request A viewpoint document extraction unit that identifies a viewpoint document having a strong relationship with the document, a relationship between documents specified by the relationship extraction unit, and a viewpoint document extracted by the viewpoint document extraction unit, and other documents in each document A score calculation means for calculating the score of each document with an index indicating the height of the relationship with the viewpoint document and an index indicating the height of the relationship with the viewpoint document in each document, and a relationship extraction means. Using the relationship between the obtained documents and the score of each document obtained by the score calculation means, the set of documents included in the document set from the entire document set and the documents related to the topic, and the set List of major topics that can be included in a document set based on information obtained by information analysis means, relationship extraction means, score calculation means, and information analysis means for identifying the role of individual documents Information output means for outputting a document indicating

The document set analysis method according to the present invention includes a document set specifying step for specifying a document set based on a user request or a predetermined condition, and a relationship between documents included in the document set specified in the document set specifying step. A relationship evaluation step for evaluating sex;
Relationships evaluated in the relationship evaluation step, relationship extraction step for identifying presence / absence of relationships between documents based on predetermined criteria, viewpoint for identifying viewpoint documents that are strongly related to the viewpoint specified by the user's request An index indicating the height of the relationship between each document and other documents using the document extraction step, the relationship between the documents specified in the relationship extraction step, and the viewpoint document extracted by the viewpoint document extraction means; The score calculation step for calculating the score of each document with an index indicating the height of the relationship with the viewpoint document in each document, the relationship between the documents obtained by the relationship extraction step, and the score calculation means Using the score of each document, the topics included in the document set from the entire document set and the set of documents related to the topic and the individual documents in the set Based on the information obtained from the information analysis step that identifies the role of the book, the relationship extraction step, the score calculation step, and the information analysis step, a list of major topics included in the document set and a document indicating the topic are output. And an information output step.

  The present invention may be a document set analysis program that causes a computer to function as the device. This program can be provided through a network or a recording medium.

  According to the present invention, it is possible to analyze a document set in consideration of the user's analysis viewpoint while considering the relationship between documents.

1 is a configuration diagram of a document set analysis apparatus according to an embodiment of the present invention. The conceptual diagram of the graph structure which shows the relationship between documents. The conceptual diagram of a three-dimensional graph structure. The processing flow figure of the document set analysis apparatus which concerns on embodiment of this invention.

  Hereinafter, a document set analysis apparatus according to an embodiment of the present invention will be described. This document set analysis apparatus gives a user viewpoint (viewpoint from the user) to the conventional document classification. Relevant documents are connected as a directed graph and scored using a random walk algorithm. The user viewpoint is reflected here, and the document with the user viewpoint has a high score. If this score can be taken vertically, it becomes a three-dimensional display with relief, and is presented to the user.

≪Example of device configuration≫
A configuration example of the document set analysis apparatus will be described with reference to FIG. Here, it is assumed that the document set analysis apparatus 1 is connected to the user client 2 through a network so that data communication is possible. The user client 2 may be a user-owned personal computer (PC) or a smartphone.

  Specifically, the document set analysis apparatus 1 is configured by a computer, and includes hardware resources of a normal computer (for example, a primary storage device such as a CPU, a RAM, and a ROM, a secondary storage device such as an HDD and an SSD, a communication device, etc. ).

  As a result of the cooperation between the hardware resource and the software resource (OS, application, etc.), the document set analysis apparatus 1 has a document set specifying unit 10, a relationship evaluation unit 20, a relationship extracting unit 30, a viewpoint document extracting unit 40, A score calculation unit 50, a graph structure construction unit 60, a vertex node extraction unit 61, a mountain node group identification unit 62, a labeling unit 63, an information output unit 70, and a document DB 100 are mounted. Hereinafter, the details of the respective units 10 to 70 will be described.

(1) Document set specifying unit 10 and relationship evaluation unit 20
The document set specifying unit 10 accesses the document DB 100 based on a request from the user input through the user client 2 or a predetermined criterion, that is, “document set condition”, and matches the “document set condition”. A document set composed of a plurality of documents is specified.

  The document DB 100 relates to a document storage device having a search function that can specify a document set according to conditions such as a search keyword designated by a user, a document last update date, document metadata, and the like. This document DB 100 may be constructed in advance in a storage device such as a hard disk drive device by collecting information in advance from the Web, or a search engine (for example, “http://www.goo.ne.jp” existing on the Web). / "Etc.) can be used as the document DB 100 as they are.

  Further, the relationship evaluation unit 20 evaluates the relationship between the documents in the document set. The relationship between documents can be generated by evaluating each document with a word vector and using cosine similarity, or constructing a language model based on one document and the probability that the other document is generated from that language model. It is possible to use the similarity between documents that are evaluated based on the language model of whether they are used, or the method that uses the closeness of the time using the time stamp of the document. The relationship may be evaluated using an index for evaluating the sex.

(2) Relationship extraction unit 30
Based on the relationship between documents evaluated by the relationship evaluation unit 20, the relationship extraction unit 30 specifies whether the documents have a relationship that satisfies a predetermined criterion. For example, when the relationship between documents is expressed as a matrix A, it can be defined as in Expression (1).

In Expression (1), “TopRel _p (i)” indicates a set of documents included in p documents having a strong relationship with the document i. Here, in general, when a document having all relevance is used, since a low relevance tends to be noise, an operation that ignores a lightly related relationship is performed.

  In the formula (1), “rel (i, j)” represents an index (standard) for evaluating the relevance between documents, and relates to an index that indicates a higher value as the relevance is higher. For example, when the similarity of contents between documents is used for relevance, it can be expressed as a word vector with a weight of “tf-idf”, and the cosine similarity between documents i and j can be used.

  Furthermore, in the above case, since it is assumed that a relationship having the highest p relevance level is used from each node (each node in the graph structure of FIG. 2 described later), the relevance level is compared with other relationships. Can be expected to exist. Therefore, it is preferable to remove a relationship having a very small degree of association from documents related to the document i. This operation is expressed by equation (2).

In Formula (2), “TopLink _q (i)” is an object to be added when documents related to document i are arranged in descending order of the degree of relevance, and the relevance is added until the threshold “q” is exceeded. It shows a set of related documents.

  The relationship between documents is expressed by the graph structure of the directed graph by the operations of the expressions (1) and (2), and when a pair of documents is considered, it is similar from one document, but from the other document. It is also possible to express the state that is not relatively similar.

(3) Perspective document extraction unit 40
The viewpoint document extracting unit 40 extracts a viewpoint document from the document set specified by the document set specifying unit 10 based on the viewpoint from the user input through the user client 2. For example, if the “viewpoint of analysis” given by the user is “I want to analyze mainly the topic that became news”, the document type is a news article and is extracted as a viewpoint document.

  Also, if the viewpoint given by the user is “I want to analyze mainly the content related to a specific topic” and a keyword representing a specific topic is given, the document containing the keyword or its Articles that are strongly related to keywords are extracted as viewpoint documents.

(4) Score calculation unit 50
The score calculation unit 50 regards the relationship between documents obtained by the relationship extraction unit 30 as the graph structure in FIG. 2 in which the document is regarded as a node and the relationship between documents is a weighted edge. The viewpoint document obtained by the unit 40 is regarded as a user's attention point, and a score is calculated in consideration of the relationship between the documents in the document group and the relationship with the viewpoint document. In the graph structure, the viewpoint of the user, that is, the viewpoint document is expressed as a viewpoint node. For example, in the graph structure of FIG. 2, some of the nodes are treated as viewpoint nodes such as nodes a1 and a2.

  For specific score calculation, “Random Walk Algorithm with Restart for Giving Bias to Specific Nodes of Graph Structure” in Non-Patent Documents 2 to 4 can be used. When calculating a score using this, a matrix expressing a transition probability in a graph structure in which a document is a node based on the relationship between documents obtained by the relationship extraction unit 30 and the relationship between documents is an edge. It calculates with Formula (3).

  Using this equation (3), the stay probability of each node is obtained by equation (4) and used as the score of each node (each document).

  In Expression (4), “P” indicates a vector expressing the score of each node. “Q” indicates a vector in which “1” is set as an element corresponding to the viewpoint document identified by the viewpoint document extraction unit 40 and “0” is set as the other elements. “Α” indicates a parameter that reduces the influence of the viewpoint document.

(5) Graph structure construction unit 60
The graph structure construction unit 60 obtains the three-dimensional graph structure shown in FIG. 3 based on the relationship between documents obtained from the relationship extraction unit 30 and the score of the node corresponding to each document obtained from the score calculation unit 50. To construct.

  At that time, first, the arrangement of nodes representing each two-dimensional planar document is determined based on the relationship between documents obtained from the relationship extraction unit 30. In order to realize this, the technique of Non-Patent Document 5 in which the similarity between nodes can be reflected as the arrangement relationship is used. Further, regarding the height of the third dimension, the coordinate in the height direction is determined based on the score calculated by the score calculation unit 50.

(6) Vertex node extraction unit 61
In the vertex node extraction unit 61, a node other than itself (one-to-one correspondence with a document) is connected from the three-dimensional graph structure constructed by the graph structure construction unit 60 by edges (edges) connecting the nodes. Nodes with higher scores are extracted as vertices.

(7) Mountain node group specifying unit 62
The mountain-shaped node group specifying unit 62 specifies a mountain (mountain shape) composed of nodes by following the graph structure in the direction in which the score decreases from the vertex node extracted by the vertex node extracting unit 61. As a result, a mountain-shaped node group constituting the mountain centering on the vertex node is specified. Details of the node extraction will be described in the labeling unit 63.
(8) Labeling part 63
The labeling unit 63 performs three types of labeling on the vertex nodes and mountain-shaped node groups extracted by the units 61 and 62 and the relationship between these nodes. Considering the graph structure and the score before labeling, according to the score definition shown by the score calculation unit 50, the relationship with the viewpoint node is strong, and the node in the area where many edges exist has a high score. .

  When considering a model in which a person walks across nodes based on the graph structure, in such an area with a high score, there are many transitions within the area, and the relevance between nodes is also high. Therefore, the area is composed of nodes related to the same topic, and each mountain in FIG. 3 is considered to correspond to a different topic. Further, it is considered that each document has a characteristic depending on the position of the node included in the mountain. In the following, the characteristics of a document corresponding to each node will be described. In addition, a method for specifying a role in a document set for each node having each characteristic will be described.

<Role of node, document characteristics>
(A) The first identified node is a vertex node at the top of the mountain in FIG. 3, that is, nodes b1 and b2, and there is always one node at the top of the mountain. These nodes b1 and b2 are nodes that are close to the viewpoint nodes a1 and a2 and have the highest state transition from the surrounding nodes, and are the nodes most closely related to the surrounding nodes. It can be said that there is.

  That is, the documents indicated by the nodes b1 and b2 specify the topic of the area, and are close to the viewpoint nodes a1 and a2, and thus can be said to be the documents that best indicate the topic related to the viewpoint that the user wants to analyze. Nodes such as nodes b1 and b2 are called core nodes, and a document indicated by the core node is called a core document.

  (B) The second specified node is a node adjacent to the peak, that is, a node that can be reached directly from the peak. For example, the nodes c1 and c2 that are the nodes constituting the mountain of FIG. 3 but not the peak can be reached directly from the core nodes b1 and b2 occupying the peak. The nodes c1 and c2 have many transitions between the core nodes b1 and b2, and the contents of the document are highly related to the core nodes b1 and b2. These nodes such as the nodes c1 and c2 are called supplemental nodes, and the document indicated by the supplemental node is called a supplemental document.

  Here, it is assumed that a node group having a sufficiently high transition probability (for example, 0.5 or more) is defined as a supplemental node with respect to a group of nodes connected from a core node by a bidirectional graph and those nodes.

  (C) The third specified node is a node linked to the core nodes b1 and b2 or the supplemental nodes c1 and c2 like the nodes d1 to d5 in FIG. This type of node corresponds to a node that has a higher probability of transition to a core node or supplemental node of a specific topic compared to state transition or self-transition to an external node, and is not necessarily the center of the topic, but is related to the topic It is a node that often includes highly novel information such as information around a topic. Nodes such as nodes d1 to d5 are called subtopic nodes, and a document indicated by the subtopic node is called a subtopic document.

  (D) The node specified last is a node that does not have a strong relationship with any topical node, that is, a node that does not form a mountain or a node that does not have a certain score. These nodes are nodes that are less relevant to other documents or are far from the viewpoint node. This node is called an outlier node, and the document indicated by the outlier node is called an outlier document.

<Labeling>
Three types of labeling that perform the above analysis are described below.

  (A) First, the first label is a label based on the position of the node in the graph structure. This is given to each node, and the labels are “core node”, “supplemental node”, “subtopic node”, and “outlier node” as described above.

  (B) The second label is given to each mountain in the three-dimensional graph structure composed of the nodes given by the aforementioned labels. A mountain consists of a core node and one or more supplemental nodes and subtopic nodes. The node group expressed as one mountain is a set of documents shown for one topic.

  Therefore, for each mountain, the second label expresses the topic described in the documents that make up the mountain. Specifically, a method is conceivable in which the title of the document corresponding to the core node is used as a mountain label.

  (C) The third label is a label given to the relationship between the peaks. First of all, it is a label indicating whether or not there is a relationship between mountains, and it is given by analyzing the relationship between mountains. When a mountain and a mountain are connected via any subtopic node or supplemental node, the connection between topics indicated by each mountain can be affirmed. In this case, by analyzing the document at the connection point, it is possible to extract a label indicating what kind of connection it has.

(9) Information output unit 70
The information output unit 70 uses the “relation between nodes”, “score of each node”, and “role in the document set” obtained by the respective units 30, 50, and 63 to generate topics in the document set. And information on a document related to the topic is output. The information output here is displayed on the screen of the user client 2 via the network and presented to the user as an analysis result.

  At this time, the three-dimensional graph structure constructed by the graph structure construction unit 60 may be used directly, or information obtained from the three-dimensional graph may be presented in a table format or the like. Here, by presenting the three-dimensional graph structure, it is possible to present the relationship between documents included in the document set and related to the viewpoint given by the user.

  For example, when a plurality of mountains are observed in a three-dimensional graph structure, it indicates that there are a plurality of topic groups and can be visually grasped. In addition, the relationship between mountains and mountains is expressed by the documents in the structure that connects the mountains and mountains, and by analyzing those documents it is possible to discover the depth of the relationship and the connection when the relationship is deep it can.

  In addition, typical information when presenting information obtained from a 3D graph structure in a tabular format etc. is included in a document set, and what cluster is divided into documents related to the viewpoint given by the user Or the content of each cluster is presented.

  As a result, according to the document set analysis apparatus 1, the document set is expressed in a graph structure, and the document set is based on (1) the relationship between documents, (2) the relationship between the viewpoint specified by the user and each document. It is possible to perform topic analysis included in. In particular, while considering the viewpoint designated by the user, it is possible to eliminate the influence of so-called “others” and to analyze a document set on which topics are concentrated.

≪Processing process≫
Hereinafter, the processing process of the document set analysis apparatus 1 will be described with reference to FIG. Here, a description will be given of a processing process in the case where the target “document set condition” and “analysis viewpoint” are input from the user client 2 via the network.

  S01: The document set analysis apparatus 1 receives “document set conditions” and “analysis viewpoint” from the user client 2 through the communication device, and accepts respective inputs. The “document set condition” input here is sent to the document set specifying unit 10, while the “analysis viewpoint” is sent to the viewpoint document extracting unit 40.

  S02 to S04: Upon receiving the “document set condition”, the document set specifying unit 10 accesses the document DB 100 and specifies a set of documents that match the “document set condition”, that is, a document group to be analyzed (S02). ). The identified document group is sent to the relationship evaluation unit 20.

  Upon receiving this identified document group, the relationship evaluation unit 20 analyzes the relationship between the documents in the document group, and sends the analysis result to the relationship extraction unit 30 (S03). Upon receiving this analysis result, the relationship extraction unit 30 specifies whether there is a relationship between documents based on the relationship between documents in the received document set and a predetermined criterion. The relationship between the specified documents is represented by a directed graph, and the document set is represented by a graph structure. This graph structure is sent to the score calculation unit 50 (S04).

  S05: Upon receipt of the “analysis viewpoint”, the viewpoint document extraction unit 40 accesses the document DB 100, identifies a document that matches the received “analysis viewpoint”, and sends the identified document group to the score calculation unit 50.

  S06: Upon receiving the graph structure of S04 and the document of S05, the score calculation unit 50 calculates the score of the node corresponding to each document of S05 based on the graph structure of S04, and the calculated score is the graph structure construction unit 60. (S06).

  S07: The graph structure construction unit 60 constructs a graph structure in which nodes are arranged in a three-dimensional space based on the graph structure in S04 and each node obtained in S06, and the constructed graph structure is a vertex node extraction unit 61. Send to.

  S08-S10: Upon receiving the graph structure of S07, the vertex node extraction unit 61 extracts a vertex node based on the received graph structure, and sends the graph structure and the vertex node to the mountain node group specifying unit 62 (S08). .

  The mountain-shaped node group specifying unit 62 that has received the graph structure and the vertex node extracts a mountain-shaped node group from the received graph structure and the vertex, and sends the extracted node group to the labeling unit 63 (S09).

  Upon receiving this node group, the labeling unit 63 performs labeling on the node group based on the graph structure, the vertex node information, and the mountain-shaped node group information, and sends the result information to the information output unit 70. (S10).

  S11: The information output unit 70 creates output data based on the information acquired in S04, S06, and S10, transmits the created data to the user client 2 via the network, and presents the result to the user.

≪Other programs, etc.≫
The present invention is not limited to the above-described embodiments, and can be applied and modified within the scope of the claims. For example, the “document set condition” and the “analysis viewpoint” may be input by the input means (keyboard, pointing device, etc.) of the document set analysis apparatus 1.

  In addition, the present invention may be configured as a document search program that causes a computer to function as a part or all of each unit 10, 20, 30, 40, 50, 60 to 63, 70, 100 of the document set analysis apparatus 1. it can. According to this program, a part or all of S01 to S11 can be executed by a computer.

  The program can be provided through a network such as a website or e-mail. The program is stored in a recording medium such as a CD-ROM, DVD-ROM, CD-R, CD-RW, DVD-R, DVD-RW, MO, HDD, BD-ROM, BD-R, or BD-RE. It is also possible to record, save and distribute. This recording medium is read using a recording medium driving device, and the program code itself realizes the processing of the above embodiment, so that the recording medium also constitutes the present invention.

DESCRIPTION OF SYMBOLS 1 ... Document set analysis apparatus 2 ... User client 10 ... Document set specific | specification part (document set specific means)
20: Relationship evaluation section (relationship evaluation means)
30 ... Relation extraction unit (relation extraction means)
40: Perspective document extraction unit (perspective document extraction means)
50 ... Score calculation unit (score calculation means)
60 ... graph structure construction unit (graph structure construction means)
61 ... Vertex node extraction unit (vertex node extraction means)
62 ... Mountain-shaped node group identifying unit (mountain-shaped node group identifying means)
63 ... Labeling part (labeling means)
70: Information output unit (information output means)
100 ... Document DB

Claims (5)

A document set analysis device that analyzes a document set of a plurality of electronic documents and identifies a topic from the document set,
A document set specifying means for specifying a document set based on a user request or a predetermined condition;
A relationship evaluation means for evaluating the relationship between each document included in the document set specified by the document set specifying means;
A relationship extraction unit that specifies the relationship evaluated by the relationship evaluation unit and the presence / absence of a relationship between documents based on a predetermined criterion;
Viewpoint document extraction means for identifying viewpoint documents strongly related to the viewpoint specified by the user's request;
Using the relationship between the documents specified by the relationship extracting means and the viewpoint document extracted by the viewpoint document extracting means, an index indicating the height of the relationship between each document and other documents, Score calculating means for calculating the score of each document with an index indicating the height of the relationship with the viewpoint document;
Using the relationship between documents obtained by the relationship extracting means and the score of each document obtained by the score calculating means, the set of documents included in the document set from the entire document set and related topics. And an information analysis means for specifying the role of each document in the set;
An information output means for outputting a list of main topics included in the document set based on the information obtained by the relationship extracting means, the score calculating means, and the information analyzing means, and a document indicating the topics;
A document set analyzing apparatus comprising: The information analysis means expresses the relationship between documents in two dimensions based on the relationship between documents and the score of each document, and arranges the score of each document in the third dimension, thereby representing the document set in a three-dimensional graph structure. Graph structure construction means expressed by
In the graph structure expressed by the graph structure construction means, a vertex node extraction means for extracting, as a vertex, a node having a higher score than a node other than itself (corresponding one-to-one with the document) connected by an edge;
A mountain-shaped node group specifying means for specifying a mountain composed of nodes by following the graph structure in a direction in which the score decreases from the vertex node extracted by the vertex node extracting means;
The document indicated by the node from the vertex node extracted by the vertex node extraction means and the mountain-like node group constituting the mountain around the vertex node, the document group constituting the mountain, and the relationship between the document groups are labeled Labeling means for attaching,
The document set analysis apparatus according to claim 1, further comprising: A document set analysis method executed by a device for analyzing a document set of a plurality of electronic documents and identifying a topic from the document set,
A document set specifying step for specifying a document set based on a user request or a predetermined condition;
A relationship evaluation step for evaluating a relationship between documents included in the document set identified in the document set identification step;
A relationship extraction step for specifying the relationship between documents based on a relationship evaluated by the relationship evaluation step and a predetermined criterion;
A viewpoint document extraction step that identifies a viewpoint document closely related to the viewpoint specified by the user's request;
Using the relationship between the documents identified by the relationship extraction step and the perspective document extracted by the perspective document extraction means, an index indicating the height of the relationship between each document and other documents, A score calculating step of calculating a score of each document with an index indicating a high relationship with the viewpoint document;
Using the relationship between documents obtained by the relationship extraction step and the score of each document obtained by the score calculation means, the set of documents included in the document set from the entire document set and related topics. And an information analysis step for identifying the role of individual documents in the set;
An information output step for outputting a list of main topics included in the document set based on the information obtained by the relationship extraction step, the score calculation step, and the information analysis step and a document indicating the topic,
A document set analysis method characterized by comprising: In the information analysis step, the relationship between documents is expressed in two dimensions based on the relationship between documents and the score of each document, and the document set is arranged in a three-dimensional graph structure by arranging the score of each document in the third dimension. Graph structure construction step expressed by
In the graph structure expressed in the graph structure construction step, a vertex node extraction step that extracts, as a vertex, a node having a higher score than a node other than itself (corresponding to a document one-to-one) connected by an edge;
A mountain-shaped node group identification step for identifying a mountain composed of nodes by following the graph structure in a direction in which the score decreases from the vertex node extracted in the vertex node extraction step;
Labels the document indicated by the node from the vertex node extracted in the vertex node extraction step and the mountain-like node group constituting the mountain centered on the vertex node, the document group constituting the mountain, and the relationship between the document group A labeling step for attaching,
The document set analysis method according to claim 1, further comprising:   A document set analysis program for causing a computer to function as the document set analysis apparatus according to claim 1.

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