Apparatus and method for sentence abstraction

Patent metadata

Field	Value
Publication number	US-2018189272-A1
Application number	US-201715851628-A
Country	US
Kind code	A1
Filing date	Dec 21, 2017
Priority date	Dec 29, 2016
Publication date	Jul 5, 2018
Grant date	—

Abstract

Disclosed are an apparatus and method for sentence abstraction. According to one embodiment of the present disclosure, the method for abstracting a sentence includes receiving a plurality of sentences including natural language; generating a sentence vector for each of the plurality of sentences by using a recurrent neural network model; grouping the plurality of sentences into one or more clusters by using the sentence vector; and generating the same sentence ID for sentences grouped into the same cluster among the plurality of sentences.

First claim

What is claimed is:

1 . A method for abstracting a sentence performed in a computing device comprising one or more processors and a memory configured to store one or more programs to be executed by the one or more processors, the method comprising:
receiving a plurality of sentences comprising natural language; generating a sentence vector for each of the plurality of sentences by using a recurrent neural network model; grouping the plurality of sentences into one or more clusters by using the sentence vector; and generating the same sentence identification (ID) for sentences grouped into the same cluster among the plurality of sentences.

2 . The method of claim 1 , wherein the recurrent neural network model comprises a recurrent neural network model of an encoder-decoder structure comprising an encoder for generating a hidden state vector from an input sentence and a decoder for generating a sentence corresponding to the input sentence from the hidden state vector.

3 . The method of claim 2 , wherein the sentence vector comprises a hidden state vector for each of a plurality of sentences generated by the encoder.

4 . The method of claim 2 , wherein the recurrent neural network model uses a latent short term memory (LSTM) unit or a gated recurrent unit (GRU) as a hidden layer unit.

5 . The method of claim 1 , wherein the grouping comprises grouping the plurality of sentences into one or more clusters based on a similarity between the sentence vectors for each of the plurality of sentences.

6 . An apparatus for abstracting a sentence, the apparatus comprising:
an inputter configured to receive a plurality of sentences comprising natural language; a sentence vector generator configured to generate a sentence vector for each of the plurality of sentences by using a recurrent neural network model; a clusterer configured to group the plurality of sentences into one or more clusters by using the sentence vector; and an ID generator configured to generate the same sentence identification (ID) for sentences grouped into the same cluster among the plurality of sentences.

7 . The apparatus of claim 6 , wherein the recurrent neural network model comprises a recurrent neural network model of an encoder-decoder structure comprising an encoder for generating a hidden state vector from an input sentence and a decoder for generating a sentence corresponding to the input sentence from the hidden state vector.

8 . The apparatus of claim 7 , wherein the sentence vector comprises a hidden state vector for each of a plurality of sentences generated by the encoder.

9 . The apparatus of claim 7 , wherein the recurrent neural network model uses a latent short term memory (LSTM) unit or a gated recurrent unit (GRU) as a hidden layer unit.

10 . The apparatus of claim 6 , wherein the clusterer is further configured to group the plurality of sentences into one or more clusters based on a similarity between the sentence vectors for each of the plurality of sentences.

Assignees

• Ncsoft Corp

Inventors

• Noh Hyung Jong
• Lee Yeon Soo
• Lee Jun Yeop
• Jang Jung Sun

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06F40/30 Primary

  Semantic analysis · CPC title

• G06F40/258

  Heading extraction; Automatic titling; Numbering · CPC title