Samsung Electronics Co., Ltd.

Dalkandura Arachchige Kalpa Shashika Silva Gunaratna

Hongxia Jin

Vijay Srinivasan

Sandeep Nama

Christian Koehler

Manas Gaur

Computer-based generation of information-gathering questions in response to a user query can include parsing the user query using natural language processing and extracting from the user query one or more phrases corresponding to a predetermined category. A knowledge database can be accessed, and entities semantically related to each phrase can be extracted therefrom. A query sub-graph representing a relationship between each of the one or more phrases and the entities extracted from the knowledge database can be generated. An expanded user query can be generated by traversing the query sub-graph. Passages semantically related to the expanded user query can be retrieved from one or more passages databases and ranked. A neural question generator can generate a set of information-gathering questions based on the expanded user query and a group of select passages selected from the plurality of passages in accordance with each selected passage's ranking.

Dynamic question generation for information-gathering

Computer-based content understanding can include segmenting an image into a plurality of blocks, wherein each block includes textual information from the image. For each block of the plurality of blocks, encoded feature data is generated by encoding visual information of the block and visual information of one or more neighboring blocks from the plurality of blocks and encoded textual data is generated by encoding the textual information of the block and the textual information of the one or more neighboring blocks. Further, using an entity class prediction model, one or more tokens of the block are classified into one or more entity classes based on a combination of the encoded textual data and the encoded feature data. A plurality of entities can be extracted from the image based on the entity classes of the plurality of blocks.

On-device knowledge extraction from visually rich documents

A method, an electronic device and computer readable medium for entity-relationship embeddings using automatically generated entity graphs instead of a traditional knowledge graph are provided. The method includes receiving, by a processor, an input text. The method also includes identifying a primary entity, a secondary entity and a context from the input text, wherein the context comprises a relationship between the primary entity and the secondary entity. The method additionally includes generating, by the processor, an entity context graph based on the primary entity, the secondary entity, and the context by: extracting, from the context, one or more text segments comprising a plurality of words describing one or more additional relationships between the primary entity and the secondary entity, and generating a plurality of context triples from the one or more text segments, each of the plurality of context triples defining a respective relationship between primary entity and the secondary entity.

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Dalkandura Arachchige Kalpa Shashika Silva Gunaratna