scholarly journals Hierarchical Annotation Event Extraction Method in Multiple Scenarios

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shi Wang ◽  
Zhujun Wang ◽  
Yi Jiang ◽  
Huayu Wang
Keyword(s):  
Random Field ◽  
Extraction Method ◽  
Feature Vector ◽  
Conditional Random Field ◽  
Event Extraction ◽  
Event Type ◽  
Event Trigger ◽  
Pipeline Model ◽  
Gated Recurrent Unit ◽  
Multiple Scenarios

In the event extraction task, considering that there may be multiple scenarios in the corpus and an argument may play different roles under different triggers, the traditional tagging scheme can only tag each word once, which cannot solve the problem of argument overlap. A hierarchical tagging pipeline model for Chinese corpus based on the pretrained model Bert was proposed, which can obtain the relevant arguments of each event in a hierarchical way. The pipeline structure is selected in the model, and the event extraction task is divided into event trigger classification and argument recognition. Firstly, the pretrained model Bert is used to generate the feature vector and transfer it to bidirectional gated recurrent unit+conditional random field (BiGRU+CRF) model for trigger classification; then, the marked event type features are spliced into the corpus as known features and then passed into BiGRU+CRF for argument recognition. We evaluated our method on DUEE, combined with data enhancement and mask operation. Experimental results show that our method is improved compared with other baselines, which prove the effectiveness of the model in Chinese corpus.

scholarly journals WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 1789-1794
Author(s):  
X. Wang ◽  
L. Xu
Keyword(s):  
Support Vector Machine ◽  
Random Field ◽  
Extraction Method ◽  
Spatial Information ◽  
Conditional Random Field ◽  
Water Extraction ◽  
Spectral Information ◽  
Support Vector ◽  
Water Index ◽  
Fully Connected

One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies from other land surface features. But conventional WI methods take into account spectral information only form a limited number of bands, and therefore the accuracy of those WI methods may be constrained in some areas which are covered with snow/ice, clouds, etc. An accurate and robust water extraction method is the key to the study at present. The support vector machine (SVM) using all bands spectral information can reduce for these classification error to some extent. Nevertheless, SVM which barely considers spatial information is relatively sensitive to noise in local regions. Conditional random field (CRF) which considers both spatial information and spectral information has proven to be able to compensate for these limitations. Hence, in this paper, we develop a systematic water extraction method by taking advantage of the complementarity between the SVM and a water index-guided stochastic fully-connected conditional random field (SVM-WIGSFCRF) to address the above issues. In addition, we comprehensively evaluate the reliability and accuracy of the proposed method using Landsat-8 operational land imager (OLI) images of one test site. We assess the method’s performance by calculating the following accuracy metrics: Omission Errors (OE) and Commission Errors (CE); Kappa coefficient (KP) and Total Error (TE). Experimental results show that the new method can improve target detection accuracy under complex and changeable environments.

“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Yan Yan ◽  
Faguo Zhou ◽  
Yifan Ge ◽  
Cheng Liu ◽  
Jingwu Feng
Keyword(s):  
Random Field ◽  
Mobile Applications ◽  
Conditional Random Field ◽  
User Reviews ◽  
Visualization System ◽  
Target Extraction

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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