Fuji Xerox Co., Ltd.

Yasuhide Miura

Tomoko Okuma

Keigo Hattori

Shigeyuki Sakaki

Noriji Kato

Motofumi Fukui

Tomoki Taniguchi

Motoki Taniguchi

Shotaro Misawa

An information processing device includes a computation unit that computes a similarity between character strings among plural character strings which express a content of electronic information and which are generated from the electronic information, and an association unit that associates a dissimilar character string that is not similar to another character string among the plural character strings with the electronic information as a representative character string that expresses the electronic information.

Information processing device and non-transitory computer readable recording medium

A purchasing behavior analysis apparatus includes an acquiring unit that acquires posting information about a specific product from posting information posted to a social networking service; a conversion unit; and an artificial neural network. The artificial neural network includes a first determination layer which determines whether a user is interested in the product, a second determination layer which determines whether the user wants the product, and a third determination layer which determines whether the user is predicted to purchase the product in the future. The purchasing behavior analysis apparatus further includes an interest presence probability calculating unit, a purchase desire probability calculating unit, and a purchase likelihood probability calculating unit.

Purchasing behavior analysis apparatus and non-transitory computer readable medium

A non-transitory computer readable medium storing a program is provided. A process of the program includes: extracting, concerning first document information including texts, a word sense associated with a word in each text; setting a word sense to be a teacher signal; creating a first topic model by using the teacher signal; estimating a topic of each text by using the first topic model; generating a learning model by using the topic as a feature and the teacher signal as a category; extracting, concerning second document information including texts, a word sense associated with a word in each text; setting a word sense to be a teacher signal; creating a second topic model by using the teacher signal; estimating a topic of each text by using the second topic model; and estimating a category of each text by using the topic as a feature and the learning model.

Non-transitory computer readable medium and information processing apparatus and method for classifying multilingual documents

An information processing apparatus includes a receiving unit, a determining unit, a first assigning unit, an extracting unit, a second assigning unit, a modeling unit, and an output unit. The receiving unit receives a target character string. The determining unit determines whether a sentiment character string is included in the received character string. A first assigning unit assigns a label corresponding to a sentiment character string to the character string when the sentiment character string is included, and assigns plural labels to the character string when no sentiment character string is included. The extracting unit extracts a word from the character string. The second assigning unit assigns to the extracted word a label which has been assigned to the character string that includes the word. The modeling unit performs supervised topic modeling for the character string. The output unit outputs a result of a process by the modeling unit.

Information processing apparatus, information processing method, and non-transitory computer readable medium

There is provided a non-transitory computer-readable medium storing a program causing a computer to execute a process. The process includes: acquiring posted information items, each of the posted information items including at least either of a text information item and an image information item; generating text information items including text items in such a manner that image information items are removed from the posted information items, and classifying the text items into first categories; generating image information items including images in such a manner that text information items are removed from the posted information items, and classifying the images into second categories; associating the classified text items and the classified images with each other on the basis of the first and second categories to obtain results; and outputting the text items and the images for each of the results.

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Kanagawa, Japan

Yasuhide Miura