Samsung Electronics Co., Ltd.

Inkyu Choi

Jehun Jeon

GyuBum Han

Jangsu Lee

A method and apparatus for training an embedding vector generation model are provided, the method includes identifying a keyword in a query sentence, generating an embedding vector of the query sentence and an embedding vector of the keyword based on the embedding vector generation model, and training the embedding vector generation model such that a first similarity between the embedding vector of the query sentence and the embedding vector of the keyword is greater than a second similarity between an embedding vector of a reference sentence that does not include the keyword and the embedding vector of the keyword.

Method and apparatus for training embedding vector generation model

A user preference modeling method including receiving preference scores corresponding to items, receiving an input for selecting an item from among the items, decaying a preference score corresponding to one or more items from among the items included in a first list and a second list based on a time decay rate and a first parameter, in response to the selected item being included in the first list, decaying the preference score corresponding to the one or more items comp included in the first list and the second list based on a time decay rate and a second parameter, in response to the selected item being included in the second list, and increasing a preference score corresponding to the selected item, wherein the first list may include one or more of the plurality of items based on the preference scores, and wherein the first list is different from the second list.

Method and apparatus for modeling user preferences

A method and device with natural language processing is disclosed. The method includes performing a word embedding of an input sentence, encoding a result of the word embedding, using an encoder of a natural language processing model, to generate a context embedding vector, decoding the context embedding vector, using a decoder of the natural language processing model, to generate an output sentence corresponding to the input sentence, generating a score indicating a relationship between the context embedding vector and each of a plurality of knowledge embedding vectors, determining a first loss based on the output sentence, determining a second loss based on the generated score, and performing training of the natural language processing model, including training the natural language processing model based on the determined first loss, and training the natural language processing model based on the determined second loss.

Method and device with natural language processing

An apparatus for supporting an electronic device is provided. The apparatus includes a first body and a second body. The first body is configured to be inserted into an insertion hole of the electronic device. The second body is configured to adjust an angle with respect to the first body, and to support the electronic device at an outside of the electronic device. A supporting angle of the electronic device is adjustable based on the angle between the first body and the second body.

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Seoul, South Korea

Inkyu Choi