Abstract: It is an object to successfully absorb a difference in characteristics to be taken into consideration between languages and implement common named entity extraction in a processing system.

Abstract: An input unit receives a morpheme array and parts-of-speech of morphemes of the morpheme array. An ambiguous word candidate acquisition unit (26) acquires, for each morpheme of the morpheme array, based on a notation and a part-of-speech of the morpheme, reading candidates of the morpheme from reading candidates of the morpheme defined in advance for each combination of a notation and a part-of-speech of the morpheme. A disambiguation unit (30) determines a reading of the morpheme from the acquired reading candidates of the morpheme by using a disambiguation rule in which a reading of the morpheme is defined in advance correspondingly to appearance positions of other morphemes and notations, parts-of-speech, or character types of the other morphemes.

Abstract: A pause estimation model learning apparatus includes: a morphological analysis unit configured to perform morphological analysis on training text data to provide M types of information, M being an integer that is equal to or larger than 2; a feature selection unit configured to combine N pieces of information, among the M pieces of information, to be an input feature when a predetermined certain condition is satisfied, and select predetermined one of the N pieces of information to be the input feature when the certain condition is not satisfied, N being an integer that is equal to or larger than 2 and equal to or smaller than M; and a learning unit configured to learn a pause estimation model by using the input feature selected by the feature selection unit and a pause correct label.

Abstract: To make it possible to generate a response sentence with respect to an input speech sentence without preparing a large amount of data. A response-type determining unit 117 determines, based on an analysis result of a speech sentence analyzed by a speech-content analyzing unit 112, a speech type indicating a type of the speech sentence and determines a response type with respect to the determined speech type based on the speech type and a type conversion rule prescribing, for each speech type, a rule for a response type with respect to a speech of the speech type. A response-sentence generating unit 119 generates the response sentence based on the response type and a response sentence database.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities and an expression unit that expresses the nonverbal information on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling the expression unit so as to express behavior corresponding to the text.

Abstract: To enable accurate estimation of a dialogue act type taking utterance subject into account. A feature value extraction unit 130 extracts feature values including an utterance subject feature value which is a feature value related to an utterance subject of an utterance sentence for each of a first utterance sentence and a second utterance sentence, the second utterance sentence being an utterance sentence preceding the first utterance sentence, including at least the utterance sentence immediately preceding the first utterance sentence. A dialogue act estimation unit 260 estimates a dialogue act type of the first utterance sentence using the aggregate feature value generated by aggregating the extracted feature values for each of the first utterance sentence and the second utterance sentence and a previously learned dialogue act estimation model for estimating the dialogue act type indicating a kind of dialogue act taking into account the utterance subject of an utterance sentence.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates nonverbal information that corresponds to feature quantities of voice or text on the basis of the feature quantities and a learned nonverbal information general model. The nonverbal information is information for controlling an expression unit that expresses behavior so that at least one of the number of times that the behavior is performed and the magnitude of the behavior correspond to the feature quantities.

Abstract: It is an object to successfully absorb a difference in characteristics to be taken into consideration between languages and implement common named entity extraction in a processing system.

Abstract: To make it possible to generate a response sentence with respect to an input speech sentence without preparing a large amount of data. A response-type determining unit 117 determines, based on an analysis result of a speech sentence analyzed by a speech-content analyzing unit 112, a speech type indicating a type of the speech sentence and determines a response type with respect to the determined speech type based on the speech type and a type conversion rule prescribing, for each speech type, a rule for a response type with respect to a speech of the speech type. A response-sentence generating unit 119 generates the response sentence based on the response type and a response sentence database.

Abstract: A nonverbal information generation apparatus includes a display unit that partitions text into predetermined units, displays the text partitioned into the predetermined units, and makes nonverbal information that represents information about behavior of a verbal output agent or nonverbal information that represents information about behavior of a receiver of verbal information of the verbal output agent that corresponds to the text when the verbal output agent outputs the verbal information visible in association with the predetermined units of the text.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling an expression unit that expresses behavior that corresponds to the text.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities and an expression unit that expresses the nonverbal information on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling the expression unit so as to express behavior corresponding to the text.