Adobe, Inc.

Somak Aditya

Atanu Ranjan Sinha

Kushal Chawla

Pranil Joshi

Bhavy Khatri

Sharmila Nangi Reddy

Abhinav Mishra

In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Creating a knowledge graph based on text-based knowledge corpora

Systems and methods for natural language processing (NLP) are described. The systems may be trained by identifying training data including clean data and noisy data; predicting annotation information using an artificial neural network (ANN); computing a loss value for the annotation information using a weighted loss function that applies a first weight to the clean data and at least one second weight to the noisy data; and updating the ANN based on the loss value. The noisy data may be obtained by identifying a set of unannotated sentences in a target domain, delexicalizing the set of unannotated sentences, finding similar sentences in a source domain, filling at least one arbitrary value in the similar delexicalized sentences, generating annotation information for the similar delexicalized sentences using an annotation model for the source domain, and applying a heuristic mapping to produce annotation information for the sentences in the target domain.

Predicting joint intent-slot structure

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Bengaluru, India

Somak Aditya