Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Jude W. Shavlik"'
Publikováno v:
SMIR@SIGIR
Detecting speculative assertions is essential to distinguish semantically uncertain information from the factual ones in text. This is critical to the trustworthiness of many intelligent systems that are based on information retrieval and natural lan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68b6cbc1737b0ac6a5a9a5acdd2fd4ae
https://doi.org/10.1142/9789813223615_0008
https://doi.org/10.1142/9789813223615_0008
Publikováno v:
Inductive Logic Programming ISBN: 9783319633411
ILP
ILP
We consider the task of KBP slot filling – extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29814a51de48b1af6b7df30167a94780
https://doi.org/10.1007/978-3-319-63342-8_7
https://doi.org/10.1007/978-3-319-63342-8_7
Publikováno v:
International Journal on Semantic Web and Information Systems. 8:42-73
Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da3248328bf35f28022b9e7bd7ce8ef9
https://doi.org/10.4018/jswis.2012070103
https://doi.org/10.4018/jswis.2012070103
Publikováno v:
Machine Learning. 86:25-56
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d288f9da1c9b05bfaa28e2c52faf3aa
https://doi.org/10.1007/s10994-011-5244-9
https://doi.org/10.1007/s10994-011-5244-9
Publikováno v:
Proceedings of the VLDB Endowment. 4:373-384
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::999c351c040701df2a1b7a7b657d121d
https://doi.org/10.14778/1978665.1978669
https://doi.org/10.14778/1978665.1978669
Autor:
Turgay Ayer, Jude W. Shavlik, Jagpreet Chhatwal, Elizabeth S. Burnside, Oguzhan Alagoz, Charles E. Kahn
Publikováno v:
Cancer. 116:3310-3321
BACKGROUND: Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48845730aff6f6239e83236581deb0c9
https://doi.org/10.1002/cncr.25081
https://doi.org/10.1002/cncr.25081
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 3:1-49
How to mine massive datasets is a challenging problem with great potential value. Motivated by this challenge, much effort has concentrated on developing scalable versions of machine learning algorithms. However, the cost of mining large datasets is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::652dfc9772b054869e4378c41e975a54
https://doi.org/10.1145/1497577.1497582
https://doi.org/10.1145/1497577.1497582
Autor:
Dolores J. Severtson, P. Flatley Brennan, Jude W. Shavlik, L. Pape, George N. Phillips, C. D. Page
Publikováno v:
Yearbook of Medical Informatics. 16:149-156
SummaryThe purpose of this paper is to describe biomedical informatics training at the University of Wisconsin-Madison (UW Madison).We reviewed biomedical informatics training, research, and faculty/trainee participation at UW-Madison.There are three
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74949f1b632a1d30d717872cd437e1fb
https://doi.org/10.1055/s-0038-1638539
https://doi.org/10.1055/s-0038-1638539
Autor:
Tushar Khot, Kristian Kersting, Jude W. Shavlik, Christopher Ré, Jose Picado, Sriraam Natarajan
Publikováno v:
Inductive Logic Programming ISBN: 9783319237077
ILP
ILP
One of the challenges to information extraction is the requirement of human annotated examples, commonly called gold-standard examples. Many successful approaches alleviate this problem by employing some form of distant supervision, i.e., look into k
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b33e8352cd99707e6e3b20a160a0ed8
https://doi.org/10.1007/978-3-319-23708-4_7
https://doi.org/10.1007/978-3-319-23708-4_7
Publikováno v:
Machine Learning. 64:231-261
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The goal of our research is to find n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4cf45edc4030f20d4e7c7138ce17b37e
https://doi.org/10.1007/s10994-006-8958-3
https://doi.org/10.1007/s10994-006-8958-3