Microsoft Technology Licensing, LLC

Cliff Richard Wong

Hoifung Poon

Sheng Zhang

Naoto Usuyama

Robin Jia

Sarthak Jain

Tristan Josef Naumann

Systems and methods are provided for generating and training a relation extraction model configured to extract document-level relations. Systems obtain a knowledge database that comprises a plurality of entity tuples and a plurality of relation types, use the knowledge database to generate annotated relation instances based on relation instances that are identified in a set of unlabeled text, generate a training dataset comprising the annotated relation instances and the set of unlabeled text, and generate the machine learning model via modular self-supervision. Systems and methods are also provided for using a relation extraction model to extract document-level relations in specific use scenarios, such as for extracting drug response relations from full-text medical research articles.

Modular self-supervision for document-level relation extraction

A computing system is provided. The computing system includes a processor configured to execute one or more programs and associated memory. The processor is configured to execute neural network system that includes a first neural network and a second neural network. The processor is configured to receive input text, and for each of a plurality of text spans within the input text: identify a vector of semantic entities and a vector of entity mentions; define an n-ary relation between entity mentions including subrelations; and determine mention-level representation vectors in the text spans that satisfy the n-ary relation or subrelations. The processor is configured to: aggregate the mention-level representation vectors over all of the text spans to produce entity-level representation vectors; input to the second neural network the entity-level representation vectors; and output a prediction of a presence of the n-ary relation for the semantic entities in the input text.

N-ary relation prediction over text spans

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Cliff Richard Wong