Dependency Tree Kernels for Relation Extraction from Natural Language Text
Lecture notes in computer sciencePublished 1 January 2009
Frank Reichartz, Hannes Korte, Gerhard Paaß
Citations34
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
New tree kernels over dependency parse trees automatically generated from natural language text with richer structural features significantly outperform all published approaches for kernel-based relation extraction from dependency trees.
Abstract
S.270-285
Keywords
Computer Science
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Cambridge University Press eBooksKernel Methods for Pattern Analysis
6,598 Citations2004John Shawe‐Taylor, Nello Cristianini
This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
International Conference on Neural Information ProcessingAdvances in kernel methods: support vector learning
5,815 Citations1999Bernhard Schölkopf, Christopher J. C. Burges +1 more
Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
Technical reportsMaking Large-Scale SVM Learning Practical
4,317 Citations2006Thorsten Joachims
This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical and give guidelines for the application of SVMs to large domains.
Automatic acquisition of hyponyms from large text corpora
3,283 Citations1992Marti A. Hearst
A set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest are identified.
Accurate unlexicalized parsing
3,055 Citations2003Dan Klein, Christopher D. Manning
It is demonstrated that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar.
Information RetrievalAn Evaluation of Statistical Approaches to Text Categorization
1,946 Citations1999Yiming Yang
Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature.
A shortest path dependency kernel for relation extraction
971 Citations2005Răzvan Bunescu, Raymond J. Mooney
Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
Dependency tree kernels for relation extraction
825 Citations2004Aron Culotta, Jeffrey Sorensen
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.
Neural Information Processing SystemsSubsequence Kernels for Relation Extraction
507 Citations2005Raymond J. Mooney, Răzvan Bunescu
A new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels, is presented, which uses three types of subsequent patterns that are typically employed innatural language to assert relationships between two entities.
Lecture notes in computer scienceEfficient Convolution Kernels for Dependency and Constituent Syntactic Trees
458 Citations2006Alessandro Moschitti
A new convolution kernel, namely the Partial Tree (PT) kernel, is proposed, to fully exploit dependency trees and an efficient algorithm for its computation is proposed which is futhermore sped-up by applying the selection of tree nodes with non-null kernel.
Corpus-based induction of syntactic structure
453 Citations2004Dan Klein, Christopher D. Manning
This work presents a generative model for the unsupervised learning of dependency structures and describes the multiplicative combination of this dependency model with a model of linear constituency that works and is robust cross-linguistically.
Lecture notes in computer scienceMachine Learning: ECML-98
398 Citations1998ECML 1998 Chemnitz, Nédellec, Claire
Lecture notes in computer scienceMissing Data in Kernel PCA
229 Citations2006Guido Sanguinetti, Neil D. Lawrence
The probabilistic interpretation of linear PCA is exploited together with recent results on latent variable models in Gaussian Processes in order to introduce an objective function for KPCA, and this new approach can be extended to reconstruct corrupted test data using fixed kernel feature extractors.
A parsing
206 Citations2003Dan Klein, Christopher D. Manning
This work presents an extension of the classic A* search procedure to tabular PCFG parsing, which is simpler to implement than an upward-propagating best-first parser, is correct for a wide range of parser control strategies and maintains worst-case cubic time.
International Joint Conference on Artificial IntelligenceShallow semantics for relation extraction
56 Citations2005Sanda M. Harabagiu, Cosmin A. Bejan +1 more
A new method for extracting meaningful relations from unstructured natural language sources based on information made available by shallow semantic parsers that surpassed the results of kernel-based models employing only semantic class information.
Exploiting Semantic Constraints for Estimating Supersenses with CRFs
16 Citations2009Gerhard Paaß, Frank Reichartz
This work uses coarse-grained supersenses of WordNet to predict these supersenses taking into account the interaction of neigboring words and alters the CRF algorithm to process lumped labels to increase the f-value.
Lecture notes in computer scienceA Logic-Based Approach to Relation Extraction from Texts
15 Citations2010Tamás Horváth, Gerhard Paaß +2 more
It is shown that an adaptation of Plotkin's least general generalization (LGG) operator can effectively be applied to such clauses and proposed a simple and effective divide-and-conquer algorithm for listing a certain set of LGGs.
Composite kernels for relation extraction
11 Citations2009Frank Reichartz, Hannes Korte +1 more
This paper shows how different kernels for parse trees can be combined to improve the relation extraction quality and on a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms all known tree kernel approaches alone.
Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining
5 Citations2008Sebastian Blohm, Philipp Cimiano
A bootstrapping approach to relation extraction which starts with a few seed tuples of the target relation and induces patterns which can be used to extract further tuples, which reduces the pattern induction complexity from quadratic to linear while mantaining extraction quality at similar or even marginally better levels.
