Entity and entity relationship recognition method and device based on deep learning

An entity relationship and deep learning technology, applied in the field of text recognition, can solve problems such as not considering that a sentence contains multiple entity relationships, and not dealing with overlapping entity relationships, so as to achieve the effect of improving accuracy

Inactive Publication Date: 2018-07-13
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, the existing supervised learning method based on deep learning only considers the relationship classification from the sentence level, so i

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  • Entity and entity relationship recognition method and device based on deep learning
  • Entity and entity relationship recognition method and device based on deep learning
  • Entity and entity relationship recognition method and device based on deep learning

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Embodiment Construction

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0034] Before introducing the entity and entity relationship recognition method and device based on deep learning, a text processing method of related technologies is briefly introduced.

[0035] With the rapid development of Internet technology, the amount of data that people need to process has increased sharply, and the phenomenon of cross-fields is prominent. How to quickly and efficiently extract effective information from texts in these open fields, so as to provide basic support for economic and social development, has beco...

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Abstract

The invention discloses an entity and entity relationship recognition method and device based on deep learning. The method comprises the following steps of inputting a text, and converting the text into a word vector, wherein the entity position, entity relationship and relationship position marking mode is adopted; performing sequence labeling on the word vector in a coding and decoding mode so as to obtain a sequence labeling word vector; performing secondary sorting on model output, wherein labels of the preset number with the highest probability of each word are selected as candidates, andlabel pairing is performed so as to obtain a correct label after pairing is successful. According to the method, the deep learning method is combined with the natural language processing technology,the multi-label and entity stacking phenomena are considered, and the brand new relationship extraction solution is put forward, so that the relationship extraction result precision is improved, and various complex conditions can be handled.

Description

technical field [0001] The present invention relates to the technical field of text recognition, in particular to a method and device for recognizing entities and entity relationships based on deep learning. Background technique [0002] With the development of the Internet, automatic information extraction from text is becoming more and more important. A typical information extraction task includes entity recognition and entity relationship recognition, and automatic relationship extraction from unstructured text forms triplets. The current methods are mainly divided into (1) rule-based methods; (2) dictionary-based methods; (3) machine learning-based methods, etc. Among them, the method based on machine learning is the current research hotspot. In the machine learning method, it is mainly divided into supervised learning method of deep learning method and weakly supervised learning method based on remote supervision. [0003] Related technologies include various methods, ...

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Application Information

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IPC IPC(8): G06F17/27
CPCG06F40/295
Inventor 鄂海红宋美娜胡莺夕王晓晖
Owner BEIJING UNIV OF POSTS & TELECOMM
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