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Convolutional neural network entity relation extraction method fusing different pre-trained word vectors

A convolutional neural network and entity relationship technology, which is applied in the field of convolutional neural network entity relationship extraction, can solve problems such as polysemy of a word

Pending Publication Date: 2021-01-05
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0006] The present invention is proposed to solve the problem of polysemy in a word in relation extraction. Different pre-trained word vectors are obtained according to different corpora and training methods. Using different pre-trained word vectors can reduce the occurrence of ambiguity

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  • Convolutional neural network entity relation extraction method fusing different pre-trained word vectors
  • Convolutional neural network entity relation extraction method fusing different pre-trained word vectors
  • Convolutional neural network entity relation extraction method fusing different pre-trained word vectors

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Embodiment Construction

[0050] In order to make the above objects, features and advantages of the present invention more comprehensible, specific implementations of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0051] Finally, the above is a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any skilled person in the art can easily understand the technical solutions of the present invention within the technical scope disclosed in the present invention. Any modification or equivalent replacement should be covered by the scope of the claims of the present invention.

[0052] Such as figure 1 , 2 As shown, a kind of convolutional neural network entity relation extraction method that fuses different pre-training word vectors that the present invention proposes, this method comprises the following steps:

[0053] Step 1: Use the input layer of the convolutional neural netw...

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Abstract

The invention discloses a convolutional neural network relation extraction method fusing different pre-trained word vectors. Relation extraction is an important semantic processing task in the field of knowledge maps. At present, the most advanced method still depends on a pre-trained word vector and a natural language processing (NLP) tool, such as a Glove word vector and a word2vec word vector to obtain sentence representation, and depends on syntactic analysis and a named entity identifier (NER) to obtain advanced characteristics. However, only one word vector is used to solve the problem of polysemy of one word, and introduction of an NLP tool certainly brings errors. In order to solve the problems, the invention provides a convolutional neural network fusing different pre-trained wordvectors, two different pre-trained word vectors and relative entity distance vectors are used as network input, the most basic convolutional neural network is adopted, no natural language processingtool is used, and the method is simple and efficient.

Description

technical field [0001] The invention belongs to the technical field of text processing, and in particular relates to a convolutional neural network entity relationship extraction method that integrates different pre-trained word vectors. Background technique [0002] Relation extraction, as one of the important tasks of information extraction, plays a vital role in many natural language processing applications, such as knowledge graphs and question answering systems. Relation extraction refers to establishing semantic relations between entity pairs in sentences, discourses or paragraphs. For example, the following sentence contains the Cause-Effect relationship. [0003] "Financial <e1>stress< / e1> is one of the main causes of <e2> divorce< / e2> " [0004] <e1> 、< / e1> , <e2> 、< / e2> The positions of entity 1 (stress) and entity 2 (divorce) in the sentence are marked. Traditional approaches treat this task as two separate su...

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Application Information

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IPC IPC(8): G06F40/295G06F40/211G06K9/62G06N3/04G06N3/08
CPCG06F40/295G06F40/211G06N3/08G06N3/045G06F18/2415
Inventor 王引苗韩志敏游科友林志赟
Owner HANGZHOU DIANZI UNIV
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