Entity and relationship joint extraction method based on reinforcement learning

A reinforcement learning, entity technology, applied in neural learning methods, unstructured text data retrieval, instruments, etc.

Active Publication Date: 2020-11-10
SICHUAN UNIV
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Problems solved by technology

[0006] Aiming at the problem of noisy data in the data set, the present inv

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  • Entity and relationship joint extraction method based on reinforcement learning
  • Entity and relationship joint extraction method based on reinforcement learning
  • Entity and relationship joint extraction method based on reinforcement learning

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with accompanying drawing:

[0048] figure 1 It is an example of sentence labeling referring to the labeling strategy proposed by Zheng et al. Before training, we need to label all training sentences according to this labeling strategy. In this labeling strategy, the label "O" is assigned to an independent word, indicating that the word is either a non-entity word or has no relationship with other entity words. Except for the "O" mark, other labels consist of three parts of information: the position of entity words, relation type and relation role. The position indicates which part of the entity the word belongs to, and the position information adopts the form of "BILS", where "B" indicates that the word is located at the beginning of the entity word, "I" indicates that the word is located in the middle of the entity word, and "E" indicates The word is at the end of the entity word. And "S" means th...

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Abstract

The invention discloses a joint information extraction method. According to the method, entity and relationship information is jointly extracted from an input text, and the method is composed of a joint extraction module and a reinforcement learning module. The joint extraction module adopts an end-to-end design and comprises a word embedding layer, a coding layer, an entity identification layer and a joint information extraction layer. Wherein the word embedding layer adopts a mode of combining a Glove pre-training word embedding library and word embedding representation based on character granularity. The encoding layer encodes the input text by using a bidirectional long-short memory network. The entity identification layer and the joint information extraction layer decode by using a one-way long-short memory network. The reinforcement learning module is used for removing noise in a data set, and a strategy network of the reinforcement learning module is composed of a convolutionalneural network. The strategy network comprises a pre-training process and a re-training process, and in the pre-training process, a pre-training data set is used for carrying out supervised training on the strategy network. In the retraining process, the strategy network is updated by obtaining the rewards of the joint extraction network, which is an unsupervised learning process.

Description

technical field [0001] The invention designs a method for jointly extracting entities and relationships based on reinforcement learning, which belongs to the technical field of natural language processing. Background technique [0002] In natural language processing tasks, entity recognition and relationship extraction are key technologies for building knowledge graphs, and are also important components of natural language processing applications such as semantic analysis, intelligent question answering, and automatic summarization. The core of this task is to extract entities of a given text and the semantic relationship between two entities. With the development of artificial intelligence and knowledge graph technology, this field has attracted the attention of more and more scholars. [0003] Traditional methods usually treat entity recognition and relation extraction tasks as two independent tasks, namely named entity recognition (Named Entity Recognition, NER) and rela...

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Application Information

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IPC IPC(8): G06F16/36G06F40/295G06F16/35G06N3/08G06N3/04
CPCG06F16/353G06F16/355G06N3/08G06N3/045
Inventor 何小海周欣刘露平罗晓东卿粼波吴小强滕奇志
Owner SICHUAN UNIV
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