scholarly journals Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Jian Wang ◽  
Xiaoyu Chen ◽  
Yu Zhang ◽  
Yijia Zhang ◽  
Jiabin Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Feature Representation ◽  
Convolutional Network ◽  
Semantic Relationships ◽  
Syntactic Information ◽  
Development And Validation ◽  
Context Features ◽  
Document Level ◽  
F Measure

BACKGROUND Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. OBJECTIVE In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. METHODS To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. RESULTS We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. CONCLUSIONS The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

scholarly journals Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

10.2196/17638 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17638
Author(s):  
Jian Wang ◽  
Xiaoyu Chen ◽  
Yu Zhang ◽  
Yijia Zhang ◽  
Jiabin Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Feature Representation ◽  
Convolutional Network ◽  
Semantic Relationships ◽  
Syntactic Information ◽  
Development And Validation ◽  
Context Features ◽  
Document Level ◽  
F Measure

Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

2021 ◽  
Vol 12 (5) ◽  
pp. 1-21
Author(s):  
Changsen Yuan ◽  
Heyan Huang ◽  
Chong Feng
Keyword(s):  
Cooperative Learning ◽  
State Of The Art ◽  
Relation Extraction ◽  
Sentence Length ◽  
Universal Relation ◽  
Dependency Parsing ◽  
Convolutional Network ◽  
Syntactic Features ◽  
Use Dependency

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

Review of methods for evaluating adaptability of buildings

2019 ◽  
Vol 37 (3) ◽  
pp. 273-287 ◽  
Author(s):  
Zoraya Roldán Rockow ◽  
Brandon Ross ◽  
Anna K. Black
Keyword(s):  
Life Cycle ◽  
Future Research ◽  
Existing Buildings ◽  
Content Type ◽  
Existing Building ◽  
Adaptation Decision ◽  
Building Life Cycle ◽  
Development And Validation ◽  
Context Features ◽  
New Buildings

Purpose The purpose of this paper is to present a review of existing models and tools for evaluating the adaptability of buildings. A baseline of the current state of the art in adaptability evaluation and adaptation decision support is established; from this baseline, gaps for future research are recommended. Design/methodology/approach A literature review was conducted to identify papers describing adaptability models and tools. The identified models were characterized based on their focus (new buildings, existing buildings, building life cycle), considered variables (physical and/or context features) and degree/type of validation. Findings Models can be grouped as those focusing on: evaluating adaptation decisions for existing buildings; the design of new buildings for future adaptation; and understanding adaptation throughout a building life cycle. Models focusing on existing building evaluation are further in development and validation than the other model types; as such, they are more suitable for use by practitioners. Another finding is that modeling of adaptability in buildings is still in its nascent stage and that data-driven quantitative modeling is a prime area for future research. Originality/value This paper is the first comprehensive review of models and tools for evaluating adaptability. Other works have evaluated the topic of adaptability more broadly, but this is the first paper to systematically characterize existing models and tools. Based on the review future, research topics are recommended.

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction

2021 ◽  
pp. 580-595
Author(s):  
Zhenyu Zhang ◽  
Bowen Yu ◽  
Xiaobo Shu ◽  
Tingwen Liu
Keyword(s):  
Reading Comprehension ◽  
Relation Extraction ◽  
Machine Reading ◽  
Document Level

scholarly journals Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (19) ◽  
pp. 2220 ◽  
Author(s):  
Ximin Cui ◽  
Ke Zheng ◽  
Lianru Gao ◽  
Bing Zhang ◽  
Dong Yang ◽  
...  
Keyword(s):  
Receptive Field ◽  
Hyperspectral Image ◽  
Receptive Fields ◽  
Feature Representation ◽  
Superior Performance ◽  
Processing Efficiency ◽  
Convolutional Network ◽  
Spatial Features ◽  
Classification Framework ◽  
Field Feature

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.

Predicting word choice in affective text

2015 ◽  
Vol 22 (1) ◽  
pp. 97-134
Author(s):  
M. GARDINER ◽  
M. DRAS
Keyword(s):  
Word Choice ◽  
Language Generation ◽  
Improve Accuracy ◽  
Increase In Accuracy ◽  
Near Synonyms ◽  
Context Features ◽  
Generation Problem ◽  
Document Level ◽  
Affective Word ◽  
Feature Frequency

AbstractChoosing the best word or phrase for a given context from among the candidate near-synonyms, such as slim and skinny, is a difficult language generation problem. In this paper, we describe approaches to solving an instance of this problem, the lexical gap problem, with a particular focus on affect and subjectivity; to do this we draw upon techniques from the sentiment and subjectivity analysis fields. We present a supervised approach to this problem, initially with a unigram model that solidly outperforms the baseline, with a 6.8% increase in accuracy. The results to some extent confirm those from related problems, where feature presence outperforms feature frequency, and immediate context features generally outperform wider context features. However, this latter is somewhat surprisingly not always the case, and not necessarily where intuition might first suggest; and an analysis of where document-level models are in some cases better suggested that, in our corpus, broader features related to the ‘tone’ of the document could be useful, including document sentiment, document author, and a distance metric for weighting the wider lexical context of the gap itself. From these, our best model has a 10.1% increase in accuracy, corresponding to a 38% reduction in errors. Moreover, our models do not just improve accuracy on affective word choice, but on non-affective word choice also.

scholarly journals A fast and effective dependency graph kernel for PPI relation extraction

2010 ◽  
Vol 11 (S5) ◽  
Author(s):  
Domonkos Tikk ◽  
Peter Palaga ◽  
Ulf Leser
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Graph Kernel

scholarly journals Document-level Relation Extraction with Dual-tier Heterogeneous Graph

2020 ◽  
Author(s):  
Zhenyu Zhang ◽  
Bowen Yu ◽  
Xiaobo Shu ◽  
Tingwen Liu ◽  
Hengzhu Tang ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Document Level

scholarly journals DocRED: A Large-Scale Document-Level Relation Extraction Dataset

2019 ◽  
Author(s):  
Yuan Yao ◽  
Deming Ye ◽  
Peng Li ◽  
Xu Han ◽  
Yankai Lin ◽  
...  
Keyword(s):  
Large Scale ◽  
Relation Extraction ◽  
Document Level
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