Abstract: [Problem] Since similarity of queries is determined on the basis of similarity of documents that are not related to a search intention, queries whose search intention is similar to each other cannot be determined. [Solution Means] A search result ranking means and a query similarity-degree calculating means are provided. The search result ranking means determines a first weight degree of each of a plurality of documents on the basis of respective evaluation results of the plurality of documents that have been retrieved by a first query, and determines a second weight degree of each of a plurality of documents on the basis of respective evaluation results of the plurality of documents that have been retrieved by a second query. The query similarity-degree calculating means calculates a similarity degree of two search results to which importance have been given, such that the similarity degree becomes larger as the documents of higher importance are similar to each other.

Abstract: Provided are a word latent topic estimation device and a word latent topic estimation method which are capable of hierarchically performing processing and which are capable of rapidly estimating latent topics of a word while taking into consideration a mixed state of topics.

Abstract: In the provided document clustering system (100), a concept tree structure accumulation unit (11) stores a concept tree structure that represents a hierarchical relationship among concepts represented by each of a plurality of words. For any two words, a concept similarity computation unit (12) obtains a concept similarity, which is an index indicating how close the concepts represented by the two words are. Using concept similarities for words that appear in two documents in a document set, an inter-document similarity computation unit (13) obtains an inter-document similarity, which indicates how similar the two documents are semantically. A clustering unit (14) uses inter-document similarities to cluster the documents in the document set.

Abstract: According to the present invention, phrases of the same kind can be extracted from a plurality of documents having various formats. A storage device stores a plurality of documents that have various formats. A pattern candidate creating unit receives a list of input words that are selected as samples among phrases that are to be included in a dictionary. The pattern candidate creating unit selects one document, determines forward and backward character strings of input words in the selected document as candidates of patterns, and stores the forward and backward character strings as a pattern candidate. The pattern candidate creating unit executes the above processes for each of the documents. A phrase candidate creating unit extracts phrases interposed between patterns included in the pattern candidate as candidates of phrases to be output, and stores the extracted phrases as a phrase candidate.

Abstract: A field pair as a combination of a definite field and an indefinite field is decided and a correlation value between the definite field and the indefinite field in each of the field pairs is calculated. Among the field pairs in which the correlation value is not smaller than a threshold value, indefinite fields having corresponding definite fields which belong to the same field group are made to be a new field group.

Abstract: A classification hierarchy regeneration system is provided, wherein when a new classification hierarchy is generated by restructuring an existing classification hierarchy, a classification hierarchy in view of hierarchical relationship of classifications and a classification hierarchy integrating classifications of the same meaning can be efficiently generated. The clustering means clusters a data group associated with a hierarchical classification, and generating a classification group, i.e., a group obtained by extracting a classification satisfying a condition defined in advance from classifications corresponding to respective data in a cluster. The cooccurrence degree calculation means calculates a degree of cooccurrence of two classifications selected from the classification group. The classification hierarchy regeneration means regenerates the hierarchy of classification based on the classification group and the degree of cooccurrence.

Abstract: In order to calculate a reliability that serves as an index of reliableness of an evaluator who evaluated a document, a reliability calculation apparatus (2) is provided with a reliability calculation unit (21) that specifies an evaluation by each evaluator with respect to each author, based on first information specifying respective correspondence relationships between documents targeted for evaluation, evaluators who evaluated the documents and contents of the evaluations, and second information specifying respective correspondence relationships between the documents and authors of the documents, and calculates the reliability of each evaluator, based on the specified evaluation with respect to each author.

Abstract: In order to accurately learn a function for evaluating documents, even in the case where sample documents having missing feature values are included as training data, a document evaluation apparatus is provided with a data classification unit (3) that classifies a set of sample documents based on missing patterns of a first feature vector, a first learning unit (4) that uses feature values that are not missing in the first feature vector and evaluation values to learn a first function for calculating a first score which is a weighted evaluation value for each classification, a feature vector generation unit (5) that computes a feature value corresponding to each classification using the first score, and generates a second feature vector having the computed feature values, and a second learning unit (6) that uses the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating documents targeted for evaluation.

Abstract: Same document group creation means (11) acquires a ratio of common words and characters between documents in order to obtain a predetermined similarity greater than a predetermined threshold value between the documents. According to the ratio, words or characters are selected with a common priority in all the documents to be matched. The documents are correlated to the same document candidate group identified by the selected words or characters and stored in a same group candidate group storage unit (22).

Abstract: The present invention more suitably determines whether a combination of words is an unexpected combination by the use of a smaller corpus. Disclosed is an unexpectedness determination system provided with: category identifying means which identifies a category to which a word belongs; category co-occurrence frequency identifying means which identifies a category co-occurrence frequency between two categories; unexpectedness index calculating means which calculates an index representing a degree of unexpectedness of a combination of two words.

Abstract: In an inverted list of each node in a taxonomy, among each node, an inverted list of the highest node is a list of integer values indicating an identifier of search subject data, and an inverted list of a node other than the highest node, in place of the identifier, is a list of integer values indicating a position in an inverted list corresponding to a node that is higher by one than the node. Furthermore, a list of integer values in an inverted list of each node is divided into two or more blocks, and a differential value between an integer value and an integer value directly before the integer value in the block is converted into a bit string of a variable length integer code.

Abstract: A word classification system is provided with an inter-word pattern learning section for learning at least either the context information or the layout information between classification-known words which co-appear and creating an inter-word pattern for determining whether data relating to a word pair which is a combination of words is data relating to a same-classification word pair which is the combination of words in the same classification or data relating to a different-classification word pair which is a combination of words in different classifications on the basis of the relationship between the classification-known words which co-appear in a document.

Abstract: Sets of strings of which the drawing positions are arranged in one direction are extracted from a document as attribute groups. An attribute name score is calculated for each attribute group to determine an extent to which each attribute group is a set of attribute names. Based on the attribute name scores, an attribute name group is selected out of the attribute groups. From among the attribute groups, an attribute group which includes a string which is the same as at least one string of the attribute name group and of which the drawing position is the same as that of the string of the attribute name group is selected. From the string at the same drawing position, an attribute name is extracted. From the other strings of the selected attribute group than those at the same drawing position, an attribute value corresponding to the attribute name is extracted.

Abstract: A cooccurrence dictionary creating system includes: a language analyzing section which subjects a text to a morpheme analysis, a clause specification, and a modification relationship analysis between clauses, a cooccurrence relationship collecting section which collects cooccurrences of nouns in each clause of the text, modification relationships of nouns and declinable words, and modification relationships between declinable words as cooccurrence relationships, a cooccurrence score calculating section which calculates a cooccurrence score of the cooccurrence relationship based on a frequency of the collected cooccurrence relationship, and a cooccurrence dictionary storage section which stores a cooccurrence dictionary in which a correspondence between the calculated cooccurrence score and the cooccurrence relationship is described.

Abstract: A communication assistance device (10) includes a communication level determination unit (11) so as to determine a level of a relationship between users who communicate with each other. The communication level determination unit (11) determines the level (communication level) of the relationship between the users based on similarity between the users obtained from preference information showing preferences of the users, and on user action records showing records of actions taken by a certain user toward a partner user with whom the certain user communicates out of the users.

Abstract: A communication assistance device (10) includes a communication level determination unit (11) and a topic recommendation unit (16) so as to determine a level of a relationship between users who communicate with each other and provide communication assistance using the result of the determination. The communication level determination unit (11) determines the level (communication level) of the relationship between the users based on similarity between the users obtained from preference information showing preferences of the users, and on user action records showing records of actions taken by a certain user toward a partner user with whom the certain user communicates out of the users. The topic recommendation unit (16) selects, from among a group of topics prepared in advance, a topic that can be transmitted to the partner user based on the determined level of the relationship between the users and on preferences of the certain user and the partner user.

Abstract: A meaning extraction device includes a clustering unit, an extraction rule generation unit and an extraction rule application unit. The clustering unit acquires feature vectors that transform numerical features representing the features of words having specific meanings and the surrounding words into elements, and clusters the acquired feature vectors into a plurality of clusters on the basis of the degree of similarity between feature vectors. The extraction rule generation unit performs machine learning based on the feature vectors within a cluster for each cluster, and generates extraction rules to extract words having specific meanings. The extraction rule application unit receives feature vectors generated from the words in documents which are subject to meaning extraction, specifies the optimum extraction rules for the feature vectors, and extracts the meanings of the words on the basis of which the feature vectors were generated by applying the specified extraction rules to the feature vectors.

Abstract: A linkage information output apparatus includes: a linkage information retrieval unit for acquiring, upon receiving source information, destination information linked with the source information, a frequency of occurrence of the source information, a frequency of occurrence of linked each of the destination information, and a frequency of occurrence of a link of the source information and each of the destination information from a linkage information accumulation unit; a recognition degree calculation unit calculating, based on each acquired frequency of occurrence, a recognition degree of the source information, a recognition degree of each acquired destination information, and a recognition degree of each link; and a high interest information narrowing unit selecting destination information to output from among each destination information based on a combination of two or more among a recognition degree of the source information, a recognition degree of the destination information, and a recognition degree

Abstract: A boundary word identification unit (103) identifies a boundary word belonging to a plurality of categories among words gathered in dictionary growth processing. Then, a category membership degree calculation unit (104) calculates, for each category to which the boundary word belongs, a category membership degree indicating a degree to which the boundary word belongs to the category on the basis of information recorded in a gathering process memory unit (108). Next, a category update unit (105) determines the category to which the boundary word belongs on the basis of the category membership degree calculated by the category membership degree calculation unit (104) and updates information stored in a gathered-by-category word memory unit (109) so that the determination result is reflected.

Abstract: When gathering words through a dictionary growth process, a dictionary growth unit (102) stores information indicating through what process of input and output a word has been gathered in a gathering process memory unit (107). Then, a clustering unit (103) classifies the word that has been gathered by the dictionary growth process into clusters on the basis of information recorded in the gathering process memory unit (107). Next, a type determination unit (104) determines whether a word comprising a cluster is of the same type as a seed word or of a different type, for each cluster into which the word has been classified, on the basis of information recorded in the gather process memory unit (107). In addition, an output unit (105) associates information indicating the gathered word, the cluster to which the word belongs and whether the cluster is of the same type as the seed word or of a different type, and displays such.