Apparatus and method for detecting debatable document
AssigneeNCSOFT Corporation
InventorsYeon Soo Lee, Jun Yeop Lee, Jung Sun Jang, Sang Min Heo
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Abstract
Disclosed are an apparatus and method for detecting a debatable document. According to an embodiment of the present disclosure, the method for detecting a debatable document includes the steps of receiving a document including one or more sentences; generating an embedding vector for each of words included in the document; and extracting features of the document from an embedding vector matrix including the embedding vectors for the words, and detecting debatability of the document from the extracted features through a detection model including a two-step convolutional neural network.
Claims14
A method for detecting a debatable document performed in a computing device comprising one or more processors and a memory storing one or more programs to be executed by the one or more processors, the method comprising:
- receiving a document comprising one or more sentences;
- generating an embedding vector for each of words included in the document;
- and
- extracting features of the document from an embedding vector matrix comprising the embedding vectors for the words, and detecting debatability of the document from the extracted features through a detection model comprising a first-step convolutional neural network and a second-step convolutional neural network,
- wherein each of the first-step convolutional neural network and the second-step convolutional neural network comprises a convolution layer.
An apparatus for detecting debatable document, the apparatus comprising:
- one or more hardware processors and one or more computer readable media storing instructions that, when executed by the one or more hardware processors, cause the apparatus to:
- receive a document comprising one or more sentences;
- generate an embedding vector for each of words included in the document;
- and
- extract features of the document from an embedding vector matrix comprising the embedding vectors for the words, and detect debatability of the document from the extracted features through a detection model comprising a first-step convolutional neural network and a second-step convolutional neural network,
- wherein each of the first-step convolutional neural network and the second-step convolutional neural network comprises a convolution layer.
Dependencies are inferred from the claim text.