Joint Extraction of Entities and Relations Based on Multi-label Classification

Liu, Yunxuan; Li, Aiping; Huang, Jiuming; Zheng, Xiaohui; Wang, Hao; Han, Weihong; Wang, Zhichao

doi:10.1109/dsc.2019.00024

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…An attention model was introduced to efectively improve the shortcomings in the literature, such as relying on part-of-speech tags. Liu et al [20] proposed a joint classifcation model based on multilabel classifcation. Te model consists of a BiLSTM + DCNN encoder and a decoder for entity, relationship, and joint entity relationship prediction.…”

Section: Knowledge Graphmentioning

confidence: 99%

Construction of Knowledge Graph for Emergency Resources

Mu,

Wu,

2024

International Journal of Intelligent Systems

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Knowledge graphs can effectively organize and represent information related to emergency resources for unforeseen sudden events. In this study, we construct a model layer for the knowledge graph of emergency resources, focused on sudden events, through the classification and analysis of unforeseen disaster measures. This study defines eight interconnected entity types, each characterised by a set of attributes and engaging in one or more relationships with other entity types. Utilizing 121 incident investigation reports from the emergency management departments of various provinces and cities over the past five years, we select five entities with the highest frequency of occurrence along with their corresponding four relationships. We then design an extraction plan for these entities and relationships. Based on the completed knowledge graph data, we formulate 14 questions related to emergency resources for sudden events and construct 19 corresponding question-and-answer templates using a template-based question-answering (QA) approach. We retrieve the corresponding Cypher statement templates through template mapping and obtain the question answers through querying. Finally, we design a knowledge graph question-and-answer system using the Django web framework, which includes entity queries and knowledge QA functions, specifically for emergency resources related to sudden events.

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Section: Knowledge Graphmentioning

confidence: 99%

Construction of Knowledge Graph for Emergency Resources

Mu,

Wu,

2024

International Journal of Intelligent Systems

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“…Figure 2 explains how the sentences are tagged by using the BIEOS scheme (Liu et al, 2019 ). In one sentence, multiple tag sequences are given, and each tag sequence only contains a triple.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The encoder part consists of a primary task-shared representation layer (PTSRL), a contextual word representation layer (CWRL), and a relation-based attention module with a gating mechanism (RBAGM). First, a tagging scheme (Liu et al, 2019 ) is applied to convert the joint extraction task to a sequence labeling problem, mapping a sentence

into a tag sequence

. Then, the PTSRL, composed of BERT (Devlin et al, 2018 ) and bidirectional long short-term memory neural network (BiLSTM) (Schuster and Paliwal, 1997 ), is used to obtain word embedding.…”

Section: Introductionmentioning

confidence: 99%

A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities

Han

Wang²,

Wang³

2022

Front. Neurorobot.

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The entity relation extraction in the form of triples from unstructured text is a key step for self-learning knowledge graph construction. Two main methods have been proposed to extract relation triples, namely, the pipeline method and the joint learning approach. However, these models do not deal with the overlapping relation problem well. To overcome this challenge, we present a relation-oriented model with global context information for joint entity relation extraction, namely, ROMGCJE, which is an encoder–decoder model. The encoder layer aims to build long-term dependencies among words and capture rich global context representation. Besides, the relation-aware attention mechanism is applied to make use of the relation information to guide the entity detection. The decoder part consists of a multi-relation classifier for the relation classification task, and an improved long short-term memory for the entity recognition task. Finally, the minimum risk training mechanism is introduced to jointly train the model to generate final relation triples. Comprehensive experiments conducted on two public datasets, NYT and WebNLG, show that our model can effectively extract overlapping relation triples and outperforms the current state-of-the-art methods.

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“…(Wadden et al, 2019) introduced a system that shares embeddings to extract named entities, builds binary relation candidates, and classifies the relation between those. (Zheng et al, 2017;Liu et al, 2019) introduce sequence label schemes to explicitly extract attributes and their relations in a single classification step. Our models similarly extract more complex structures without an enumeration of relation candidates.…”

Section: Related Workmentioning

confidence: 99%

“…Our second approach is motivated by (Liu et al, 2019). They applied a sequence labeling approach instead of sequence tagging.…”

Section: Span Labelingmentioning

confidence: 99%

Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction

Klöser,

Kohl,

Kraft

et al. 2021

Preprint

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Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.

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Joint Extraction of Entities and Relations Based on Multi-label Classification

Cited by 8 publications

References 12 publications

Construction of Knowledge Graph for Emergency Resources

Construction of Knowledge Graph for Emergency Resources

A Relation-Oriented Model With Global Context Information for Joint Extraction of Overlapping Relations and Entities

Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction

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