National Institute of Information and Communications Technology, Incorporated

Stijn De Saeger

Kentaro Torisawa

Chikara Hashimoto

Jonghoon Oh

Junichi Kazama

Masaki Murata

Motoki Sano

Kiyonori Ootake

Kow Kuroda

Takao Kawai

Takuya Kawada

Yiou Wang

Masaaki Tsuchida

Istvan Varga

[Object] To provide a system for automatically and reliably collecting information belonging to a given category, and matching the information appropriately in a timely manner. [Solution] A text classifying deviceanalyzes dependency of collected texts by a morpheme analyzing unitand a dependency analyzing unit. A problem report collecting unitspecifies a core consisting of noun+predicate in a text based on dependency relation of the text, and using a combination of noun classification (trouble/non-trouble) and predicate classification (excitatory/inhibitory), classifies the text to a problem report or the rest, by a method referred to as core-based matrix. Support information collecting deviceand request message collecting devicecollect support information and request messages in the similar manner. A matching devicematches problem reports and support information collected by problem report collecting unitand support information collecting deviceby a method referred to as co-occurrence core matrix, and thus associates problem information (support information) with appropriate support information (problem information).

Text matching device and method, and text classification device and method

A question-answering system capable of appropriately answering a non-factoid question. The question-answering system responsive to a question, for taking out answer candidates from an object document storage unit; extracting plausible ones from the answer candidates; recognizing causal relation expression included in extracted answer candidates; determining whether or not a recognized causal relation is relevant as an answer; generating a feature vector related to a combination of the question and the searched answer candidate; and for calculating, when a feature vector is given, a score indicating a degree of how correct an answer candidate is as an answer to the question, for the question and the answer candidate as a source of the feature vector, and for outputting, as the answer to the question, an answer candidate having the highest score.

Non-factoid question-answering system and method

In order to provide a non-factoid question answering system with improved precision, the question answering system () includes: a candidate retrieving unit (), responsive to a question, extracting answer candidates from a corpus storage (); a feature vector generating unit () for generating features from combinations of a question with each of the answer candidates; SVMs () trained to calculate a score of how correct a combination of the question with an answer candidate is, upon receiving the feature vector therefor; and an answer ranker unit () outputting the answer candidate with the highest calculated score as the answer. The features are generated on the basis of the results of morphological analysis and parsing of the question, a phrase in the question evaluated as being positive or negative as well as its polarity, and the semantic classes of nouns in the features.

Non-factoid question-answering system and computer program

A predicate template collector allowing efficient and automatic recognition of predicate templates is adapted to include: a noun pair collectorand a noun pair polarity determinerfor collecting noun pairs co-occurring with predicate template pairs and determining polarity of relation between nouns, using conjunctions and seed templates; a template pair collector, collecting template pairs co-occurring with noun pairs and determining, based on the relation of noun pairs co-occurring with the predicate template pairs and the conjunctions between predicate templates pairs, whether the polarity of excitatory class of predicate template pair is the same or not; a template network builderbuilding a template network connecting predicate templates based on the predicate template pairs and match/mismatch of excitatory class thereof; and a template excitation value calculatorcalculating excitation value to be assigned to each node, using the excitation value of seed templates and the relation between each of the nodes in the network.

Predicate template collecting device, specific phrase pair collecting device and computer program therefor

A relationship information expansion apparatus capable of acquiring a new relationship based on a relationship information piece including two or more language expressions having a semantic relationship is provided. The relationship information expansion apparatus generates a candidate expanded relationship information piece in which at least one language expression included in the relationship information piece was replaced with a similar language expression, and acquires a score that indicates a probability that the candidate expanded relationship information piece has a semantic relationship. The relationship information expansion apparatus selects an expanded relationship information piece, which is a candidate expanded relationship information piece having a high score among candidate expanded relationship information pieces.

Relationship information expansion apparatus, relationship information expansion method, and program

Conventionally, it has been impossible to appropriately acquire word pairs having a prescribed relationship. Such word pairs can be appropriately acquired with a word pair acquisition apparatus including: a word class information storage unit in which word class information can be stored; a class pair favorableness degree storage unit in which a class pair favorableness can be stored; a seed pattern storage unit in which can be stored one or more seed patterns; a word pair acquisition unit that acquires one or more word pairs co-occurring with the seed pattern from sentence groups; a class pair favorableness degree acquisition unit that acquires a class pair favorableness degree; a score determination unit that uses the class pair favorableness degree to determine a score of each of the word pairs; and a word pair selection unit that acquires one or more word pairs having a high score.

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Tokyo, Japan

Stijn De Saeger