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Generation of sentence representation

專利號(hào)
US11176333B2
公開日期
2021-11-16
申請(qǐng)人
International Business Machines Corporation(US NY Armonk)
發(fā)明人
Bang An; HongLei Guo; Shiwan Zhao; Zhong Su
IPC分類
G06F40/56; G06F40/30; G06F40/289; G06F40/58
技術(shù)領(lǐng)域
sentence,graph,word,nodes,node,syntactic,in,neighbor,may,cloud
地域: NY NY Armonk

摘要

Embodiments of the present disclosure relate to generation of sentence representation. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words, the sentence graph comprising nodes representing the words and edges connecting the nodes to indicate relationships between the words. Word representations for the plurality of words are determined based on the sentence graph by applying a graph convolution operation on respective sets of neighbor nodes for respective ones of the nodes, a set of neighbor nodes for a node having edges connected with the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

說明書

For example, in the sentence graph 512 illustrated in the examples of FIGS. 6A and 6B, for the given node 610-1, its set of neighbor nodes includes this node 610-1 itself (because there is an undirected/directed edge indicating the self-relationship) and the node 610-2. For the given node 610-2, its neighbor nodes include the nodes 610-1, 610-2, 610-3, and 610-5 except the node 610-4. The respective sets of neighbor nodes for other nodes in the sentence graph 512 may also be identified accordingly.

The graph convolution module 526 is configured to apply a graph convolution operation on the set of neighbor nodes to obtain the word representation for the given node. By means of the graph convolution operation, information of the neighbor nodes can be passed to the given node to generate the corresponding word representation. The graph convolution module 526 may be designed to utilize of any convolution operations that can be employed to process graph information. In some embodiments, the graph convolution module 526 may be implemented based on a neural network which can implement representation extraction from a graph. Such neural network may also be referred to as a graph neural network (GNN). The graph convolution module 526 may be implemented as one or more layers in the GNN to perform the graph convolution operation.

權(quán)利要求

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