Boundary assembling method for joint entity and relation extraction

A region-based hypergraph network for joint entity-relation extraction

Named entity recognition and relation extraction are two important core subtasks of information extraction, which aim to identify named entities and extract relations between them. In recent years, span representation methods have received a lot of attention and are widely used to extract entities and corresponding relations from plain texts. Most recent works focus on how to obtain better span representations from pre-trained encoders, but ignore the negative impact of a large number of span candidates on slowing down the model performance. In our work, we propose a joint entity and relation extraction model with an attention mechanism and position-attentive markers. The attention score of each candidate span is calculated, and most of the candidate spans with low attention scores are pruned before being fed into the span classifier, thus achieving the goal of removing the most irrelevant spans. At the same time, in order to explore whether the position information can improve the performance of the model, we add position-attentive markers to the model. The experimental results show that our model is effective. With the same pre-trained encoder, our model achieves the new state-of-the-art on standard benchmarks (ACE05, CoNLL04 and SciERC), obtaining a 4.7%-17.8% absolute improvement in relation F1.

We present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in sequence, our system incorporates information from relation extraction into entity extraction. This means that the system can be trained even on data sets where only a subset of all valid entity spans is annotated.

Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.

Boundary regression model for joint entity and relation extraction

Joint entity and relation extraction based on a hybrid neural network

ERGM: A multi-stage joint entity and relation extraction with global entity match

Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties. To solve the problems of complex relation types of domain entities, the lack of a large amount of annotated corpus, and the difficulty of extraction, this study proposed a method for constructing domain-annotated datasets based on publicly available texts on the web, which integrates remote supervision and semi-supervision. For the relational triad extraction of a given core entity (an entity lexicon defined semi-automatically by experts), an inflated gate attention network structure for increasing the perceptual field of the model is proposed. In addition, a relational extraction model, Ro-DGANet, was designed based on this structure, incorporating the idea of a probability graph. The Ro-DGANet model was experimentally evaluated on the publicly available Chinese datasets LIC2019 and CHIP2020 and compared with the mainstream relation extraction models, achieving the best results with F1 values of 82.99% and 66.39%, respectively. Finally, the Ro-DGANet model was applied to the relation extraction task of equipment components in industrial scenarios and to the relation extraction task of core knowledge points of programming languages. The analysis results show that the proposed method is applicable to open relation extraction among core entities in different domains with reliable performance and portability.

Relation Extraction (RE) and Entity Typing (ET) are two important tasks in natural language processing field. Existing methods for RE and ET usually handle them separately. However, relation extraction and entity typing have strong relatedness with each other, since entity types are informative for inferring relations between entities, and the relations can provide important information for predicting types of entities. Exploiting the relatedness between relation extraction and entity typing has the potential to improve the performance of both tasks. In this paper, we propose a neural network based approach to jointly train relation extraction and entity typing models using a multi-task learning framework. For relation extraction, we adopt a piece-wise Convolutional Neural Network model as sentence encoder. For entity typing, since there are multiple entities in one sentence, we design a couple-attention model based on Bidirectional Long Short-Term Memory network to obtain entity-specific representation of sentences. In our MTL frame, the two tasks share not only the low-level input embeddings but also the high-level task-specific semantic representations with each other. The experiment results on benchmark datasets demonstrate that our approach can effectively improve the performance of both relation extraction and entity typing.

Joint entity and relation extraction with position-aware attention and relation embedding

Biomedical relation extraction from scientific publications is a key task in biomedical natural language processing (NLP) and can facilitate the creation of large knowledge bases, enable more efficient knowledge discovery, and accelerate evidence synthesis. In this paper, building upon our previous effort in the BioCreative VIII BioRED Track, we propose an enhanced end-to-end pipeline approach for biomedical relation extraction (RE) and novelty detection (ND) that effectively leverages existing datasets and integrates state-of-the-art deep learning methods. Our pipeline consists of four tasks performed sequentially: named entity recognition (NER), entity linking (EL), RE, and ND. We trained models using the BioRED benchmark corpus that was the basis of the shared task. We explored several methods for each task and combinations thereof: for NER, we compared a BERT-based sequence labeling model that uses the BIO scheme with a span classification model. For EL, we trained a convolutional neural network model for diseases and chemicals and used an existing tool, PubTator 3.0, for mapping other entity types. For RE and ND, we adapted the BERT-based, sentence-bound PURE model to bidirectional and document-level extraction. We also performed extensive hyperparameter tuning to improve model performance. We obtained our best performance using BERT-based models for NER, RE, and ND, and the hybrid approach for EL. Our enhanced and optimized pipeline showed substantial improvement compared to our shared task submission, NER: 93.53 (+3.09), EL: 83.87 (+9.73), RE: 46.18 (+15.67), and ND: 38.86 (+14.9). While the performances of the NER and EL models are reasonably high, RE and ND tasks remain challenging at the document level. Further enhancements to the dataset could enable more accurate and useful models for practical use. We provide our models and code at https://github.com/janinaj/e2eBioMedRE/. Database URL: https://github.com/janinaj/e2eBioMedRE/.

Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.

We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks. We study the performance of this approach on 28 datasets, spanning 10 structure prediction tasks including open information extraction, joint entity and relation extraction, named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, factual probe, intent detection, and dialogue state tracking. We further enhance the pretraining with the task-specific training sets. We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets that we evaluate.

In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential and regional features of the text, and a complete word graph is further utilized to extract implicit features among all word pairs of the text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show that GraphRel maintains high precision while increasing recall substantially. Also, GraphRel outperforms previous work by 3.2% and 5.8% (F1 score), achieving a new state-of-the-art for relation extraction.

Entity relationship extraction (ERE) plays a crucial role in natural language processing and has great practical significance, especially when applied to electronic medical records (EMRs). Traditional methods usually use hierarchical structures or local decision‐making processes and often perform poorly in complex situations such as Single Entity Overlap (SEO) and Entity Pair Overlap (EPO), resulting in inaccuracies and loss of information. To overcome these limitations, this study introduced URRel, a combination model of entity and relationship extraction that combines unified coding and reinforcement learning (RL) techniques. URRel uses a semantic fusion approach inspired by UniRel to merge sentences and relational descriptions into a single representation, allowing the ability to simultaneously capture entities and their relationships. An interaction matrix was also built to model multiple dependencies between entity pairs and between entities and relationships to improve the model's ability to understand complex relational patterns. URRel views relationship extraction as a sequential decision‐making process and uses an Actor‐Critic RL algorithm to dynamically optimise the extraction strategy and reduce the possibility of decoding errors accumulating over time. Experimental results on the China coronary EMR private dataset (CACMeD) and the publicly available DuIE relational extraction dataset show that URRel significantly outperforms several leading baseline models, yielding F1 scores of 80.35% and 77.87% on DuIE and private dataset CACMeD. This research emphasises the effectiveness of combining unified semantic representation with RL, providing a novel framework for solving complex information extraction challenges.