Neural relation extraction: a review

Aydar, Mehmet; Bozal, Özge; Özbay, Furkan

doi:10.3906/elk-2005-119

Cited by 7 publications

(6 citation statements)

References 35 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, deep learning methods in particular can deal with the feature sparsity problem by transforming features into low-dimensional dense vectors. Thus, deep learning models have exhibited superior performances compared to the traditional machine learning-based and rule-based models [10,67]. The most used machine learning models are Long Short-Term Memory (LSTM), CRF, Graph Convolutional Network (GCN) and SVM.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

show abstract

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

show abstract

“…Relation Extraction (RE) is the task to predict the semantic relations between given subjective and objective entities, namely head and tail entities, in the context Wang et al (2021); Aydar et al (2020); Cui et al (2017). A typical RE example can be to discern the relation between the head entity "SpaceX" and the tail entity "Elon Musk", given the sentence "SpaceX was founded in 2002 by Elon Musk".…”

Section: Re-annotation In Relation Extractionmentioning

confidence: 99%

Pre-trained Language Models as Re-Annotators

Chen¹

2022

Preprint

View full text Add to dashboard Cite

Annotation noise are widespread in datasets, but manually revising a flawed corpus is time-consuming and error-prone. Hence, given the prior knowledge in Pre-trained Language Models and the expected uniformity across all annotations, we attempt to reduce annotation noise in the corpus through two tasks automatically: (1) Annotation Inconsistency Detection that indicates the credibility of annotations, and (2) Annotation Error Correction that rectifies the abnormal annotations. We investigate how to acquire semantic sensitive annotation representations from Pre-trained Language Models, expecting to embed the examples with identical annotations to the mutually adjacent positions even without fine-tuning. We proposed a novel credibility score to reveal the likelihood of annotation inconsistencies based on the neighbouring consistency. Then, we fine-tune the Pre-trained Language Models based classifier with cross-validation for annotation correction. The annotation corrector is further elaborated with two approaches: (1) soft labelling by Kernel Density Estimation and (2) a novel distant-peer contrastive loss. We study the re-annotation in relation extraction and create a new manually revised dataset, Re-DocRED, for evaluating document-level re-annotation. The proposed credibility scores show promising agreement with human revisions, achieving a Binary F 1 of 93.4 and 72.5 in detecting inconsistencies on TACRED and DocRED respectively.Moreover, the neighbour-aware classifiers based on distant-peer contrastive learning and uncertain labels achieve Macro F 1 up to 66.2 and 57.8 in correcting annotations on TACRED and DocRED respectively. These improvements are not merely theoretical: Rather, automatically denoised training sets demonstrate up to 3.6% performance improvement for state-of-the-art relation extraction models, and the proposed framework is expected to be hundreds of times faster than the human re-annotators empirically.

show abstract

“…the task definition. 1 Existing RE surveys mainly focus on modeling techniques (Bach and Badaskar, 2007;Pawar et al, 2017;Aydar et al, 2021;Liu, 2020). To the best of our knowledge, we are the first to give a comprehensive overview of available RE datasets.…”

Section: Introductionmentioning

confidence: 99%

What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification

Bassignana¹,

Plank²

2022

Preprint

View full text Add to dashboard Cite

Over the last five years, research on Relation Extraction (RE) witnessed extensive progress with many new dataset releases. At the same time, setup clarity has decreased, contributing to increased difficulty of reliable empirical evaluation (Taillé et al., 2020). In this paper, we provide a comprehensive survey of RE datasets, and revisit the task definition and its adoption by the community. We find that crossdataset and cross-domain setups are particularly lacking. We present an empirical study on scientific Relation Classification across two datasets. Despite large data overlap, our analysis reveals substantial discrepancies in annotation. Annotation discrepancies strongly impact Relation Classification performance, explaining large drops in cross-dataset evaluations. Variation within further sub-domains exists but impacts Relation Classification only to limited degrees. Overall, our study calls for more rigour in reporting setups in RE and evaluation across multiple test sets. 1 We refer the reader to Luo et al. (2016) for a survey on biomedical RE and event extraction.2 Pyysalo et al. ( 2008) discuss similar difficulties in the biomedical domain.

show abstract

Neural relation extraction: a review

Cited by 7 publications

References 35 publications

Information extraction from electronic medical documents: state of the art and future research directions

Information extraction from electronic medical documents: state of the art and future research directions

Pre-trained Language Models as Re-Annotators

What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification

Contact Info

Product

Resources

About