Deep Neural Approaches to Relation Triplets Extraction: a Comprehensive Survey
TL;DR: More recently, with the advances made in the continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction as discussed by the authors.
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Abstract: The task of relation extraction is about identifying entities and relations among them in free text for the enrichment of structured knowledge bases (KBs). In this paper, we present a comprehensive survey of this important research topic in natural language processing. Recently, with the advances made in the continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction. To help future research, we present a comprehensive review of the recently published research works in relation extraction. Previous surveys on this task covered only one aspect of relation extraction that is pipeline-based relation extraction approaches at the sentence level. In this survey, we cover sentence-level relation extraction to document-level relation extraction, pipeline-based approaches to joint extraction approaches, annotated datasets to distantly supervised datasets along with few very recent research directions such as zero-shot or few-shot relation extraction, noise mitigation in distantly supervised datasets. Regarding neural architectures, we cover convolutional models, recurrent network models, attention network models, and graph convolutional models in this survey. We survey more than 100 publications in the field of relation extraction and present them in a structured way based on their similarity in the specific task they tried to solve, their model architecture, the datasets they used for experiments. We include the current state-of-the-art performance in several datasets in this paper for comparison. In this paper, we have covered different aspects of research in relation extraction field with a key focus on recent deep neural network-based methods. Also, we identify possible future research directions. Hopefully, this will help future researchers to identify the current research gaps and take the field forward.
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Citations
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RTE: A Tool for Annotating Relation Triplets from Text.
TL;DR: In this article, a web-based relation triplets extractor (RTE) is proposed for extracting structured information about real-world entities from the unstructured text available on the Web.
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A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
Tapas Nayak,Hwee Tou Ng +1 more
TL;DR: The authors proposed cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities, and they also proposed a hierarchical entity graph convolutional network (HEGCN) model for this task.
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Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering
TL;DR: This article proposed a self-ensemble filtering mechanism to filter out the noisy samples during the training process, which improves the robustness of neural relation extraction models and increases their F1 scores.
References
Double Graph Based Reasoning for Document-level Relation Extraction
Shuang Zeng,Runxin Xu,Baobao Chang,Lei Li +3 more
- 01 Nov 2020
TL;DR: This paper proposes Graph Aggregation-and-Inference Network (GAIN) featuring double graphs, based on which GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document and proposes a novel path reasoning mechanism to infer relations between entities.
Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research
TL;DR: It is proposed that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.
Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
Tapas Nayak,Hwee Tou Ng +1 more
- 03 Apr 2020
TL;DR: A representation scheme for relation tuples which enables the decoder to generate one word at a time like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities.
Drug-drug interaction extraction from biomedical texts using long short-term memory network.
Sunil Kumar Sahu,Ashish Anand +1 more
TL;DR: In this paper, three LSTM-based models, namely B-LSTM, AB-LstM, and Joint AB-lstm, were proposed for drug-drug interaction (DDI) extraction.
202
An analysis of open information extraction based on semantic role labeling
Janara Christensen,Stephen Soderland,Oren Etzioni +2 more
- 26 Jun 2011
TL;DR: This work investigates the use of semantic role labeling techniques for the task of Open IE and compares SRL-based open extractors with TextRunner, an open extractor which uses shallow syntactic analysis but is able to analyze many more sentences in a fixed amount of time and thus exploit corpus-level statistics.