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Stacked Ensembles of Information Extractors for Knowledge-Base Population

Vidhoon Viswanathan, Nazneen Fatema Rajani, Yinon Bentor, Raymond Mooney
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)  
We present results on using stacking to ensemble multiple systems for the Knowledge Base Population English Slot Filling (KBP-ESF) task.  ...  Additionally, we demonstrate that including provenance information further increases the performance of stacking.  ...  Acknowledgements We thank the anonymous reviewers for their valuable feedback. This research was supported by the DARPA DEFT program under AFRL grant FA8750-13-2-0026.  ... 
doi:10.3115/v1/p15-1018 dblp:conf/acl/ViswanathanRBM15 fatcat:ixyuybrpjbcnhmvnfa22o6gwhi

Stacked Ensembles of Information Extractors for Knowledge-Base Population by Combining Supervised and Unsupervised Approaches

Nazneen Fatema Rajani, Raymond J. Mooney
2015 Text Analysis Conference  
Our system uses stacking to ensemble multiple systems for the KBP slot filling task, as described in our ACL 2015 paper.  ...  We believe this combination approach gives our best run for the ensembling task. In this paper, we also discuss strategies to handle Cold Start data which comes from multiple hops.  ...  Introduction In 2015, UT Austin was a first time participant in the Slot Filler Validation/Ensembling task of the Text Analysis Conference (TAC) Knowledge Base Population (KBP) evaluation.  ... 
dblp:conf/tac/RajaniM15 fatcat:jop54i4prrchjfmvkcvl434jre

Supervised and Unsupervised Ensembling for Knowledge Base Population [article]

Nazneen Fatema Rajani, Raymond J. Mooney
2016 arXiv   pre-print
We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity  ...  stacking approach to ensembling KBP systems.  ...  Recently, using stacking (Wolpert, 1992) to ensemble IE systems was shown to give state-of-the-art results on slot-filling for Knowledge Base Population (KBP) (Viswanathan et al., 2015) .  ... 
arXiv:1604.04802v1 fatcat:2epjnk5acfbyhfz56enwxjjmvq

A hybrid ontology-based information extraction system

Fernando Gutierrez, Dejing Dou, Stephen Fickas, Daya Wimalasuriya, Hui Zong
2016 Journal of information science  
In the case of the integration strategy, we propose to integrate the outputs of both implementations under the ensemble learning schema of stacking.  ...  Ontology-based Information Extraction (OBIE), a subfield of IE, mitigates this difficulty by integrating domain knowledge by using a domain ontology.  ...  Comparison of correct statement extraction and error extraction functionality in terms of precision, recall and F1 measure for information extractors with single implementation (ER and ML), multiple implementations  ... 
doi:10.1177/0165551515610989 fatcat:bnfhhmvsrza7tjowbemd6r4fem

A Study of Concept Extraction Across Different Types of Clinical Notes

Youngjun Kim, Ellen Riloff, John F Hurdle
2015 AMIA Annual Symposium Proceedings  
We compare two types of ensemble methods (Voting/Stacking) and a domain adaptation model, and show that a Stacked ensemble of classifiers trained with i2b2 and specialty data yields the best performance  ...  Our research investigates methods for creating effective concept extractors for specialty clinical notes.  ...  We thank Jennifer Thorne, RN and Jenifer Williams, RN for their annotation work, and Dr. Stéphane Meystre for his comments and feedback.  ... 
pmid:26958209 pmcid:PMC4765588 fatcat:zeki4hfkongllaw5owsb744m4y

FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph [article]

Majid Asgari-Bidhendi, Behrooz Janfada, Behrouz Minaei-Bidgoli
2020 arXiv   pre-print
To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of  ...  In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabeled raw text, crawled from the Web.  ...  Related Work Knowledge Base Population is defined as the process of extending a knowledge base with information extracted from the text.  ... 
arXiv:2005.01879v1 fatcat:6dgj5ch6jbfd3kw7omblxblwfe

One-shot Transfer Learning for Population Mapping [article]

Erzhuo Shao, Jie Feng, Yingheng Wang, Tong Xia, Yong Li
2021 arXiv   pre-print
Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control.  ...  In knowledge transfer scenario, we employ single reference fine-grained ground truth in the target city as the ground truth to inform the large-scale urban structure and support the knowledge transfer  ...  PSRNet consists of three major components: STNet for model-based knowledge transfer, PGNet for data-based transfer, and pixel-level adversarial domain adaptation (PADA) for optimization-based knowledge  ... 
arXiv:2108.06228v1 fatcat:kug3pogrnzcfpnxitdw4b6eo74

Semantic Data Ingestion for Intelligent, Value-Driven Big Data Analytics

Jeremy Debattista, Judie Attard, Rob Brennan
2018 2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data)  
In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles).  ...  creating AI ensembles.  ...  ACKNOWLEDGEMENT We would like to thank Giovanni Schiuma, Markus Helfurt, Pieter De Leenheer, Eamonn Clinton, Diego Calvanese, Christian Dirschl, Ismael Caballero, Hans Viehmann, and Rico Richter for their  ... 
doi:10.1109/innovate-data.2018.00008 dblp:conf/obd/DebattistaAB18 fatcat:3hiyg2rd4fh2hdx37jelozkz5e

Stacking With Auxiliary Features [article]

Nazneen Fatema Rajani, Raymond J. Mooney
2016 arXiv   pre-print
In this paper, we propose stacking with auxiliary features that learns to fuse relevant information from multiple systems to improve performance.  ...  Ensembling methods are well known for improving prediction accuracy. However, they are limited in the sense that they cannot discriminate among component models effectively.  ...  We seek to integrate knowledge from multiple sources for improving ensembles of systems using Stacking with Auxiliary Features (SWAF).  ... 
arXiv:1605.08764v1 fatcat:5a43zcqjfvcirh35a5xzrbq35e

Combining one-class classifiers via meta learning

Eitan Menahem, Lior Rokach, Yuval Elovici
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier.  ...  Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier  ...  TUPSO and the best-base classifier populate the cluster that represents the top-tier classification performance.  ... 
doi:10.1145/2505515.2505619 dblp:conf/cikm/MenahemRE13 fatcat:za5bfu3bwzhxtm5gimg4jg4y3e

Stacking With Auxiliary Features

Nazneen Fatema Rajani, Raymond J. Mooney
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Ensembling methods are well known for improving prediction accuracy. However, they are limited in the sense that they cannot effectively discriminate among component models.  ...  In this paper, we propose stacking with auxiliary features that learns to fuse additional relevant information from multiple component systems as well as input instances to improve performance.  ...  Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2017/367 dblp:conf/ijcai/RajaniM17 fatcat:e742hljldjbxbjmdapmfxswnd4

Pixel-Level Weed Classification Using Evolutionary Selection of Local Binary Pattern in a Stochastic Optimised Ensemble

Basil Andy Lease, W. K. Wong, Lenin Gopal, Choo W. R. Chiong
2020 SN Computer Science  
This model design allows for different feature inputs selected by GA for each of the NN classifiers in the ensemble, unlike classical ensembles which share the same input data.  ...  The model design is based on an ensemble with a two-level optimisation structure.  ...  Chiong for their assistance in running the experiments to compare against non-evolutionary ensemble classifiers as recommended by the reviewers.  ... 
doi:10.1007/s42979-020-00357-y fatcat:cthwnzkyhzagve7d6ruijnqzzy

Concurrent evolution of feature extractors and modular artificial neural networks

Victor Hannak, Andreas Savakis, Shanchieh Jay Yang, Peter Anderson, Teresa H. O'Donnell, Misty Blowers, Kevin L. Priddy
2009 Evolutionary and Bio-Inspired Computation: Theory and Applications III  
Although the underlying mathematics of ANNs is well understood, customization based on theoretical analysis is impractical because of the complex interrelationship between ANN behavior and the problem  ...  First, ANNs require customization for each specific application.  ...  A variety of methods are available to accomplish the combination of multiple networks to create an ensemble [Sharkey96] [Hashem93] including averaging, non-linear combination and stacked generalization  ... 
doi:10.1117/12.820008 fatcat:pfjt3lx2nvgz7eij7gh4j7z5ve

Ensemble-based learning using few training samples for video surveillance scenarios

C. A. Mitrea, S. Carata, B. Ionescu, T. Piatrik, M. Ghenescu
2015 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)  
To cope with these challenges we investigate three established ensemble-based learning techniques, e.g., boosting, bagging and blending (stacking).  ...  The article targets the task of content-based multiple-instance people retrieval from video surveillance footage.  ...  Ensemble Based Training We investigate three state-of-the-art ensemble based learning techniques, e.g., boosting, bagging and blending (sometimes named stacking).  ... 
doi:10.1109/ipta.2015.7367104 dblp:conf/ipta/MitreaCIPG15 fatcat:adix6a2fn5awxjcefdnoc4s2be

SwiSpot: modeling riboswitches by spotting out switching sequences

Marco Barsacchi, Eva Maria Novoa, Manolis Kellis, Alessio Bechini
2016 Bioinformatics  
Although there are a handful of known riboswitches, our knowledge in this field has been greatly limited due to our inability to identify them based on its sequence.  ...  Moreover, it is able to model the switching behavior of riboswitches whose generated ensemble covers both alternate configurations.  ...  Funding The work was partially supported by individual institutional funds from the University of Pisa. E.M.N. is supported by an HFSP Postdoctoral Fellowship (LT000307/2013-L).  ... 
doi:10.1093/bioinformatics/btw401 pmid:27378291 fatcat:gcojk4fckjazdnnjrscbzofuju
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