Proceedings Article10.3115/1218955.1219009
Dependency Tree Kernels for Relation Extraction
Aron Culotta,Jeffrey Sorensen +1 more
- 21 Jul 2004
- pp 423-429
985
TL;DR: This work extends previous work on tree kernels to estimate the similarity between the dependency trees of sentences, and uses this kernel within a Support Vector Machine to detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles.
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Abstract: We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20% F1 improvement over a "bag-of-words" kernel.
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Citations
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Ranjay Krishna,Yuke Zhu,Oliver Groth,Justin Johnson,Kenji Hata,Joshua Kravitz,Stephanie Chen,Yannis Kalantidis,Li-Jia Li,David A. Shamma,Michael S. Bernstein,Li Fei-Fei +11 more
TL;DR: The Visual Genome dataset as mentioned in this paper contains over 108k images where each image has an average of $35$35 objects, $26$26 attributes, and $21$21 pairwise relationships between objects.
•Posted Content
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Ranjay Krishna,Yuke Zhu,Oliver Groth,Justin Johnson,Kenji Hata,Joshua Kravitz,Stephanie Chen,Yannis Kalantidis,Li-Jia Li,David A. Shamma,Michael S. Bernstein,Fei-Fei Li +11 more
TL;DR: The Visual Genome dataset is presented, which contains over 108K images where each image has an average of $$35$$35 objects, $$26$$26 attributes, and $$21$$21 pairwise relationships between objects, and represents the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.
1.6K
•Proceedings Article
Open information extraction from the web
Michele Banko,Michael Cafarella,Stephen Soderland,Matt Broadhead,Oren Etzioni +4 more
- 06 Jan 2007
TL;DR: Open Information Extraction (OIE) as mentioned in this paper is a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input.
Open information extraction for the web
Oren Etzioni,Michele Banko +1 more
- 01 Jan 2009
TL;DR: Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input, is introduced.
Visual Relationship Detection with Language Priors
Cewu Lu,Ranjay Krishna,Michael S. Bernstein,Li Fei-Fei +3 more
- 08 Oct 2016
TL;DR: In this article, the authors propose a model that uses this insight to train visual models for objects and predicates individually and later combines them together to predict multiple relationships per image and localize the objects in the predicted relationships as bounding boxes in the image.
References
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
Vladimir Vapnik
- 01 Jan 1998
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
30.4K
•Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Nello Cristianini,John Shawe-Taylor +1 more
- 01 Jan 2000
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
15K
•Book
Introduction to Statistical Pattern Recognition
Keinosuke Fukunaga
- 01 Jan 1972
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
12.1K
•Book
An Introduction to Support Vector Machines
Nello Cristianini,John Shawe-Taylor +1 more
- 01 Mar 2000
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
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