Named entity relation extraction and construction method based on deep learning

A technology of relation extraction and named entity, applied in the field of Internet information, to reduce the interference of cluttered information, improve performance and high accuracy

Active Publication Date: 2014-12-10
中科嘉速(北京)信息技术有限公司
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Problems solved by technology

[0006] The present invention provides a named entity relationship extraction and construction method based on deep learning in order to solve the problems of domain-specific indexing data set acquisition, pattern acquisition and coreference resolution existing in the existing entity relationship extraction technology

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  • Named entity relation extraction and construction method based on deep learning
  • Named entity relation extraction and construction method based on deep learning
  • Named entity relation extraction and construction method based on deep learning

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Embodiment Construction

[0024] The present invention will be further described in detail below with reference to the drawings and embodiments.

[0025] In the embodiment of the present invention, the method for extracting and constructing a named entity relationship based on deep learning of the present invention is explained in conjunction with the specific field of automobiles. Including: segmentation of auto news text collection; based on self-learning bootstrap method to extract entity pairs (car brand, car model) from segmentation units obtained from word segmentation, and select a small number of examples from them as the initial seed set; bootstrap-based method Extract relationship templates from entities; use deep learning technology to construct relationships between entities, and cluster / classify relationship templates to obtain relationship classifications.

[0026] Such as figure 1 As shown, according to the specific field of the embodiments of the present invention, the deep learning-based na...

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Abstract

The invention provides a named entity relation extraction and construction method based on deep learning, which is applied to the technical field of internet information. The method includes, to a specific field, capturing news data within the field from a vertical website and preprocessing the captured news data; segmenting the news data, extracting key words to generate a field lexicon, and segmenting the news data again according to the field lexicon; extracting a seeded lexicon; constructing an entity relation network in an unsupervised manner, extracting sentences containing at least two entities from the news data, extracting verbs of the sentences and corresponding documents, building a word clustering model based on deep learning to the extracted documents, and constructing the entity relation network according to relation between words described by the verbs; and finally defining category of the entity relation and performing relation classification to each entity pair of the entity relation network. Without input of large-scale manpower to mark sample data, dependence to the corpus is reduced and performance of entity relation extraction is high.

Description

Technical field [0001] The present invention relates to the field of Internet information technology, in particular, to a method for extracting named entity relationships. Background technique [0002] In the field of information research, information extraction technology is an indispensable key technology. Facing such a large amount of information space, how to extract the content that users are interested in faster and more accurately is an urgent problem to be solved, and it is also an important research direction of information mining technology. Information extraction is different from information processing technology such as information retrieval. It needs to identify the named entities of the text and extract the relationships between entities. However, the flexibility of words in Chinese texts, complex word formation and no obvious signs, make It is more difficult to identify Chinese named entities and extract relationships. [0003] At present, there are two main metho...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/211G06F16/285G06F16/288G06F16/951
Inventor 袁伟邓攀闫碧莹赵鑫李玉成余雷
Owner 中科嘉速(北京)信息技术有限公司
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