Samsung Electronics Co., Ltd.

Vineeth Thanikonda Munirathnam

Pavan Sudheendra

Balvinder Singh

Sanjay Narasimha Murthy

Ravi Prasad Mohan Kini

Gururaj Bhat

Girish R Kulkarni

Rajiv Baronia

Debayan Mukherjee

Narasimha Gopalakrishna Pai

Pankaj Kumar Bajpai

Swadha Jaiswal

Girish Kulkarni

Shivi Pal

Mannu Amrit

Vibha Venkatesh Kulkarni

Jack Porter

Suzanne M Kirch

Soundararajan Velu

Raviprasad Mohan Kini

Semantic frame identification involves associating identified target words in the sentential context of their natural language source with semantic frames from a frame lexical database. The disclosed invention leverages the CapsNet architecture for improved semantic frame identification of a target word in a natural language input. This includes deriving the features of a target word identified in the sentence and extracting the features of the word units and the thematic words around the target word. Through dynamic routing of capsules, the CapsNet is able to filter the candidate frames for the target word to reduce the search space and apply the CapsNet prediction to identify a frame from a frame lexical database.

Semantic frame identification using capsule networks

Named Entity Recognition is the identification and classification of named entities within a document. Nested NEs occur when an NE is contained within another NE. The disclosed invention leverages the CapsNet architecture for improved nested NE identification and classification. This includes deriving the features of an input text. The derived features are used to identify and classify any named entities in the text. The system is further configured to identify named entities in the text and perform clustering to group named entities. The disclosed CapsNet considers the context of the whole text to activate higher capsule layers in order to identify the named entities and classify them. The teachings of this invention are applicable to other NER models to improve nested NE identification and classification.

Nested named entity recognition

A method for switching between a first lens and a second lens in an electronic device includes displaying, by the electronic device, a first frame showing a field of view (FOV) of the first lens; detecting, by the electronic device, an event that causes the electronic device to transition from displaying the first frame to displaying a second frame showing a FOV of the second lens; generating, by the electronic device and based on the detecting the event, at least one intermediate frame for transitioning from the first frame to the second frame; and switching, by the electronic device and based on the detecting the event, from the first lens to the second lens and displaying the second frame, wherein the at least one intermediate frame is displayed after the displaying the first frame and before the displaying the second frame while the switching is performed.

Method and electronic device for switching between first lens and second lens

Embodiments herein disclose a method for recommending an image capture mode by an electronic device. The method includes identifying, by the electronic device, at least one ROI displayed in a camera preview of the electronic device for capturing an image in a non-ultra-wide image capture mode. Further, the method includes determining, by the electronic device, that the at least one ROI is suitable to capture in an ultra-wide image capture mode. Further, the method includes providing, by the electronic device, at least one recommendation to switch to the ultra-wide image capture mode from the non-ultra-wide image capture mode for capturing the image.

Method and electronic device for recommending image capture mode

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Karnataka, India

Vineeth Thanikonda Munirathnam