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.

Abstract: The information processing device 1 processes document collections having tags permitting semantic class identification appended to each document and comprises a search unit 2, which creates multiple semantic class units containing one, two, or more semantic classes based on a taxonomy that identifies relationships between semantic classes, and a frequency calculation unit 3 which, for each of the semantic class units, identifies documents that match that semantic class unit in the document collections and, for these matching documents, calculates a first frequency that represents the frequency of occurrence in a designated document collection and a second frequency that represents the frequency of occurrence in non-designated document collections. Once the calculations have been performed, the search unit 2 identifies any of the semantic class units based on the first frequency and the second frequency of the matching documents.

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: To provide a technique for structuralizing ontology in a prescribed form to a structure to which features of data are reflected. An ontology processing device has a structuralizing device for structuralizing properties of the ontology in the prescribed form generated from a set of instance data containing a combination of a subject, a property, and an object expressed with a character string according to the features of the object, and has a ontology storage device which stores the ontology structuralized by the structuralizing device. With this structure, the properties of the ontology in the prescribed form are corrected or expressed as an ontology structure by reflecting the characteristics of a set of the objects obtained from the data.

Abstract: An information classification device (1) is provided with an union of sets determination unit (10) which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit (11) which applies a plurality of individual determination rules to the content to be classified which has been determined as correct, determines whether the content matches the condition, and performs correct/incorrect determination again regarding the content to be classified which has been determined as correct on the basis of the determination result of each individual determination rule. The union of sets determination rule is created using a result of correct/incorrect determination previously performed by two or more people with respect to a plurality of contents which are different from the contents to be classified, and also using feature amounts of respective different contents.

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: A base station apparatus that estimates whether or not each terminal device belonging to a communication cell of the present apparatus is present in an end area of the cell.

Abstract: The disclosed apparatus uses a training data generation apparatus 2, which generates training data used for creating characteristic expression extraction rules. The training data generation apparatus 2 includes: a training data candidate clustering unit 21, which clusters a plurality of training data candidates assigned labels indicating annotation classes based on feature values containing respective context information, and a training data generation unit 22 which, by referring to each cluster obtained using the clustering results, obtains the distribution of the labels of the training data candidates within the cluster, identifies training data candidates that meet a preset condition based on the obtained distribution, and generates training data using the identified training data candidates.

Abstract: A data classifier system of the present invention selects a plurality of classifications correlated to data groups so as to output classification axes based on hierarchical classifications and data groups. The data classifier system includes a basic category accumulation means, a classification axis candidate creation means and a priority calculation means. The basic category accumulation means accumulates classifications serving as basic categories used for selecting desired classifications in advance. The classification axis candidate creation means creates classification axis candidates based on combinations of classifications each correlated to at least one data among descendant classifications of each basic category. The priority calculation means calculates priorities with respect to the classification axis candidates created by the classification axis candidate creation means based on hierarchical distances of classifications in the classified hierarchy.

Abstract: An information analysis apparatus, an information analysis method, and a program are provided that enable target information to be determined in units of single sentences, rather than in units of plural sentences, while taking into consideration the tendency of appearance of the target information. An information analysis apparatus 5 is used to perform an analysis on text information to determine whether or not the text information corresponds to the target information.

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 data classifier system of the present invention selects a plurality of classifications correlated to data groups so as to output classification axes based on hierarchical classifications and data groups. The data classifier system includes a basic category accumulation means, a classification axis candidate reduction means and a priority calculation means. The basic category accumulation means accumulates classifications serving as basic categories used for desired classifications in advance. The classification axis candidate reduction means selects a plurality of classifications from among classifications descendant from each basic category so as to create classification axis candidates, thus reducing classification axis candidates subjected to calculations based on data quantity of classifications and hierarchical distances of classifications.

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 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 transmission power control system comprises a communication state monitor circuit and a transmission power control bit adjusting circuit connected to the communication state monitor circuit. The communication state monitor circuit detects quality deterioration of a communication state of radio communication between a base station and mobile stations. The transmission power control bit adjusting circuit adjusts the transmission power control bit signals so as to suppress increase of transmission power of the mobile stations when the quality deterioration detector detects the quality deterioration.

Abstract: To provide a technique for structuralizing ontology in a prescribed form to a structure to which features of data are reflected. An ontology processing device has a structuralizing device for structuralizing properties of the ontology in the prescribed form generated from a set of instance data containing a combination of a subject, a property, and an object expressed with a character string according to the features of the object, and has a ontology storage device which stores the ontology structuralized by the structuralizing device. With this structure, the properties of the ontology in the prescribed form are corrected or expressed as an ontology structure by reflecting the characteristics of a set of the objects obtained from the data.