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Domain-Targeted, High Precision Knowledge Extraction

Bhavana Dalvi Mishra, Niket Tandon, Peter Clark
2017 Transactions of the Association for Computational Linguistics  
produces high precision knowledge targeted to a particular domain - in our case, elementary science.  ...  To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that  ...  Most importantly, the pipeline offers for the first time a viable way of extracting large amounts of high-quality knowledge targeted to a specific domain.  ... 
doi:10.1162/tacl_a_00058 fatcat:ielbe2fbpvhb3jbp57oo6jha7q

Domain-Targeted, High Precision Knowledge Extraction

Bhavana Dalvi, Mishra Tandon, Peter Clark
unpublished
produces high precision knowledge targeted to a particular domain in our case, elementary science.  ...  To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that  ...  Most importantly, the pipeline offers for the first time a viable way of extracting large amounts of high-quality knowledge targeted to a specific domain.  ... 
fatcat:yoa4hgyxobgqrplq6frg6s3gna

CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews [article]

Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu
2016 arXiv   pre-print
The domain knowledge helps the unsupervised method to adapt to different products and greatly improves the precision of the CER task.  ...  Then we expand category-level domain knowledge about complementary entities using only a few general seed verbs on a large amount of unlabeled reviews.  ...  The idea of using domain knowledge is that high precision dependency paths can expand high quality (precision) domain knowledge on a large amount of unlabeled reviews, which in turn helps low precision  ... 
arXiv:1612.01039v1 fatcat:kbrl5ndiineyniesahquxn2sze

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Dongchuan Yan, Hao Zhang, Guoqing Li, Xiangqiang Li, Hua Lei, Kaixuan Lu, Lianchong Zhang, Fuxiao Zhu
2021 Remote Sensing  
However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information.  ...  for information extraction.  ...  Relational knowledge transfer involves the mapping of relevant knowledge between the source domain and the target domain.  ... 
doi:10.3390/rs14010103 fatcat:rtzlszmpavbxjh4k5trk45yzdq

Transfer learning for cancer diagnosis in histopathological images

Sandhya Aneja, Nagender Aneja, Pg Emeroylariffion Abas, Abdul Ghani Naim
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
Densenet161 has been shown to have high precision whilst Resnet101 has a high recall.  ...  A high precision model is suitable to be used when follow-up examination cost is high, whilst low precision but a high recall/sensitivity model can be used when the cost of follow-up examination is low  ...  INTRODUCTION Transfer learning focuses on transferring latent knowledge from a source domain to a target domain, to solve the issue of insufficient data on the target domain.  ... 
doi:10.11591/ijai.v11.i1.pp129-136 fatcat:rdjuerhmp5gb7khf25emeij6ne

Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data [article]

Zhiyuan Wu, Yu Jiang, Chupeng Cui, Zongmin Yang, Xinhui Xue, Hong Qi
2021 arXiv   pre-print
capability by considering images from both the target and the proximity domains as input.  ...  high-precision accuracy boost by 1.4% and 8.2% respectively, with 78.2% segmentation variance) with only 41.8% FLOPs (see Fig. 1).  ...  When the value of r is set small, i.e. the target domain accounts for a smaller proportion of the images used for feature extraction, the final obtained Ss tend to gain a higher high-precision accuracy  ... 
arXiv:2103.13733v2 fatcat:mmnmt5zeoneqlg3g324mebx45e

Text mining agent for net auction

Yukitaka Kusumura, Yoshinori Hijikata, Shogo Nishida
2004 Proceedings of the 2004 ACM symposium on Applied computing - SAC '04  
The first problem is that if the system collects items automatically, the results contain the items which is different from the items of the user's target.  ...  The system collects Web pages of items and extracts the items' features from the pages. After that, it generates a table which contains the extracted features.  ...  For realizing information extraction with high precision, we use domain knowledge 1 about the items for every category.  ... 
doi:10.1145/967900.968124 dblp:conf/sac/KusumuraHN04 fatcat:dnmit3ymirgenbx2l5xuccchxe

Knowledge Graph-Based Image Recognition Transfer Learning Method for On-Orbit Service Manipulation

Ao Chen, Yongchun Xie, Yong Wang, Linfeng Li
2021 Space Science & Technology  
dense source domain data and sparse target domain data, and can be transferred to the test dataset containing large number of data collected from target domain.  ...  The average recognition precision of the proposed method is 80.5%, and the average recall is 83.5%, which is higher than that of ResNet50-FC; the average precision is 60.2%, and the average recall is 67.5%  ...  It can be seen that using ResNet50-FC and proposed KGTL methods, precision and recall for image recognition in the source domain are high, both higher than 98.1%.  ... 
doi:10.34133/2021/9807452 fatcat:w3yy6qdv4vfutpwyb7utvky6oi

Adaptive Inception Based on Transfer Learning for Effective Visual Recognition

Balaji Sreenivasulu, Visvesvaraya Technological University, Anjaneyulu Pasala, Gaikwad Vasanth, Infosys Labs, Government Engineering College
2020 International Journal of Intelligent Engineering and Systems  
In computer vision, domain adaptation or transfer learning plays an important role because it learns a target classifier characteristics using labeled data from various distribution.  ...  The transfer learning approach is used to enhance the results of the first phase and the Support Vector Machine (SVM) is used to learn all the features extracted from inception layers.  ...  However, the method failed to extract the high level features of the target domain, when the background is complex.  ... 
doi:10.22266/ijies2020.1231.01 fatcat:qwucummy3rcofbnkile3eqh5qi

A Multi-Domain Web Text Feature Extraction Model for e-Science Environment

Weng Yu
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
To validate the performance, the experiments on the multi-domain text feature extraction, topic features dynamical tracking and the domain knowledge cooperative scheduling demonstrate that the model has  ...  Through cooperative scheduling the domain knowledge between different local data centers, the knowledge utilization efficiency of the domain information in the global scope is improved sharply.  ...  The traditional text features extraction method mostly relied on domain dictionary to realize the word segmentation of the target text, this method is simple and feasible for the specific domain knowledge  ... 
doi:10.14257/ijsip.2015.8.11.38 fatcat:6wmvtawn45eopiraukyayn7gna

HDSKG: Harvesting domain specific knowledge graph from content of webpages

Xuejiao Zhao, Zhenchang Xing, Muhammad Ashad Kabir, Naoya Sawada, Jing Li, Shang-Wei Lin
2017 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)  
The extraction of domain specific relation triples (subject, verb phrase, object) is one of the important techniques for domain specific knowledge graph construction.  ...  Knowledge graph is useful for many different domains like search result ranking, recommendation, exploratory search, etc.  ...  The key challenge in construction of the knowledge graph is the extraction of relation triples with high precision and recall.  ... 
doi:10.1109/saner.2017.7884609 dblp:conf/wcre/ZhaoXKSLL17 fatcat:x7yacqpifndxhntw7muwuegeoa

SAR Target Detection Based on Domain Adaptive Faster R-CNN with Small Training Data Size

Yuchen Guo, Lan Du, Guoxin Lyu
2021 Remote Sensing  
In the proposed method, in order to make full use of the label information and realize more accurate domain adaptation knowledge transfer, an instance level domain adaptation constraint is used rather  ...  from optical remote sensing images to SAR images, and develop a domain adaptive Faster R-CNN for SAR target detection with small training data size.  ...  high-level semantic features in both source and target domains.  ... 
doi:10.3390/rs13214202 doaj:a2d43a04c2464102b59bff6fd4256444 fatcat:p3bsrpjemjfjfj5oi6au5gcxw4

Web-based extraction of semantic relation instances for terminology work [chapter]

Jakob Halskov, Caroline Barrière
2010 Benjamins Current Topics  
Finally, the article examines the domain-dependence of different aspects of the pattern-based knowledge discovery approach proposed.  ...  domain.  ...  High precision; 2. High recall; 3. High portability.  ... 
doi:10.1075/bct.23.02hal fatcat:ckytf5nz3fcr3dxocjur3lxhwa

Multi-Source Domain Adaptation in Sentiment Analysis using Optimized Neural Network and Cross-Domain Semantic Library

Dipak Patel, Vishwakarma Government Engineering College
2021 International Journal of Intelligent Engineering and Systems  
Remaining features are then given to proposed classifier that predicts the polarity of target domain in a precise way. For classification purpose Neural Network (NN) is exploited.  ...  The higher order statistics based features extraction includes use of modified cross entropy measure.  ...  The semantic knowledge graph determines the probability among the target domain and each source domains.  ... 
doi:10.22266/ijies2021.1031.47 fatcat:4mgiubzk25e4vf62kmu3447m7u

Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data

Zhongmin Deng, Xinjie Zhang, Yanlin Zhao
2020 Sensors  
A readily available fault diagnosis dataset is selected as ancillary knowledge to train a high-precision mapping from FR data to updating parameters.  ...  Finite element model updating precision depends heavily on sufficient vibration feature extraction.  ...  Hence, a high-precision inverse mapping from raw FR data to the updating parameter is proposed to overcome precision diminution in artificial feature extraction [8] .  ... 
doi:10.3390/s20195615 pmid:33019561 fatcat:wvvnenlhg5aw3pcbay2rwvyhyu
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