Patent application EP17173957A (EP), filed 2017-06-01
Publication number: EP3258390A1
Title: MEANING GENERATION METHOD, MEANING GENERATION APPARATUS, AND STORAGE MEDIUM
Abstract: A meaning generation method, in a meaning generation apparatus, includes acquiring meaning training data including text data of a sentence that can be an utterance sentence and meaning information indicating a meaning of the sentence and associated with the text data of the sentence, acquiring restatement training data including the text data of the sentence and text data of a restatement sentence of the sentence, and learning association between the utterance sentence and the meaning information and the restatement sentence. The learning includes learning of a degree of importance of a word included in the utterance sentence, and the learning is performed by applying the meaning training data and the restatement training data to a common model, and storing a result of the learning as learning result information.
IPC codes: G06F  17/    27            A I
Claims: number: 1
text: A meaning generation method in a meaning generation apparatus, the meaning generation method comprising:
number: 2
text: The meaning generation method according to claim 1, further comprising:
number: 3
text: The meaning generation method according to claims 1 or 2, wherein the first meaning training data is acquired from a first corpus in which two or more pieces of meaning training data are accumulated.
number: 4
text: The meaning generation method according to any of claims 1 to 3, wherein the first restatement training data is acquired from a second corpus in which two or more pieces of restatement training data are accumulated.
number: 5
text: The meaning generation method according to any of claims 1 to 4, wherein the model is a neural network model.
number: 6
text: The meaning generation method according to claim 5, wherein the learning is performed by performing error backpropagation learning between (i) the meaning information and the second sentence associated with the first sentence and (ii) the posterior probability, calculated using the model, for the meaning information and the second sentence corresponding to the first sentence.
number: 7
text: The meaning generation method according to claim 1, wherein the learning of association between the utterance sentence and the restatement sentence is performed using internal information obtained in the learning of the association between the utterance sentence and the meaning information.
number: 8
text: The meaning generation method according to claim 7, wherein the model is a neural network model, and
the internal information is a weight between layers in the neural network model.
number: 9
text: The meaning generation method according to claim 1, wherein at least one of the acquiring of the first meaning training data, the acquiring of the first restatement training data, and the learning of the association, and the storing of the learning result information is performed by a processor.
number: 10
text: A meaning generation apparatus comprising:
number: 11
text: A non-transitory computer-readable storage medium storing a program causing a computer to execute a process comprising:
Office action: search_report, dated 2017-12-20
Prior art cited: ref_id: JP5362651B2
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ref_id: - JIANPENG CHENG ET AL, "Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning", PROCEEDINGS OF THE 2015 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, Stroudsburg, PA, USA, (20150917), doi:10.18653/v1/D15-1177, pages 1531 - 1542, XP055423744 [I] 1-11 * abstract * * sections 1, 3, 4, 6, 7.4 *
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severity: background
Final disposition: pending (2017-12-20)