Lg Electronics Inc.

Samsung Sds Co., Ltd.

Tuv Sud Korea Ltd.

Jaewoong Yun

Daesung Kim

HyunJae Lee

Hyunjin Choi

Heeyeon Nah

Bongkyu Hwang

Judong Kim

Chaegyeong Park

Jahyong Choi

Byunghyun Park

Minkyu Shim

An apparatus for processing natural language includes a collection module for collecting a document having style information on text in the document, a preprocessing module for extracting style information from the text of the collected document, and labeling the extracted style information with the text, and a first machine learning module receiving the text labeled with the style information, and predicting a position of a word having the style information in the received text.

Apparatus and method for processing natural language

A document summarizing apparatus includes an encoding unit receiving document data comprised of one or more sentences and converts the document data into a token defined in a predetermined unit to generate a feature vector, an extraction summary unit receiving the feature vector and calculating a probability value that each sentence corresponds to a summary, with respect to each one or more sentences constituting the document data, and generating an attention vector for each token weight based on the probability value, and a decoding unit receiving the feature vector and the attention vector and generating abstract summary data.

Method and apparatus for document summarization

A vehicle collision test method comprises the steps of: setting a speed and a target acceleration of a target vehicle; observing a speed and an acceleration of a subject vehicle corresponding to the set speed and target acceleration of the target vehicle; decelerating the subject vehicle and calculating a time-to-collision (TTC) when a first threshold time is exceeded after deceleration of the target vehicle; and storing as a risk scenario including information on the subject vehicle when the calculated time-to-collision is shorter than a second threshold time longer than the first threshold time.

Vehicle collision test method

Provided is a method of recognizing a business card by a terminal through federated learning, including receiving an image of the business card; extracting a feature value from the image including text related to a field of an address book set in the terminal; inputting the feature value into a first common prediction model and determining first text information from an output of the first common prediction model; analyzing a pattern of the first text information and inputting the first text information into the field; caching the first text information and second text information received for error correction of the first text information from a user; and training the first common prediction model using the image, the first text information, and the second text information, whereby each terminal may train and share the first common prediction model.

Method and apparatus for recognizing a business card using federated learning

Provided is a method for recognizing handwritten characters in a terminal through federated learning. In the method, a first common prediction model for recognizing text from handwritten characters input from a user is applied, the handwritten characters are received from the user, feature values are extracted from an image including the handwritten characters, the feature values are input to the first common prediction mode, first text information is determined from an output of the first common prediction model, the first text information and a second text information received from the user for error correction of the first text information are cached, and the first common prediction model is learned using the image including the handwritten characters, the first text information, and the second text information. In this way, the terminal can determine the text from the handwritten characters input by the user, and can learn the first common prediction model through a feedback operation of the user.

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Seoul, South Korea

Jaewoong Yun