Joint Entity and Relation Extraction Using Card-Pyramid Parsing

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. We present a new method for joint entity and relation extraction using a graph we call a "card-pyramid." This graph compactly encodes all possible entities and relations in a sentence, reducing the task of their joint extraction to jointly labeling its nodes. We give an efficient labeling algorithm that is analogous to parsing using dynamic programming. Experimental results show improved results for our joint extraction method compared to a pipelined approach.

[1]  Fang Kong,et al.  Exploiting Constituent Dependencies for Tree Kernel-Based Semantic Relation Extraction , 2008, COLING.

[2]  Erik F. Tjong Kim Sang,et al.  Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition , 2002, CoNLL.

[3]  Rohit J. Kate,et al.  Using String-Kernels for Learning Semantic Parsers , 2006, ACL.

[4]  Michael Collins,et al.  Ranking Algorithms for Named Entity Extraction: Boosting and the VotedPerceptron , 2002, ACL.

[5]  Rohit J. Kate,et al.  Comparative experiments on learning information extractors for proteins and their interactions , 2005, Artif. Intell. Medicine.

[6]  Razvan C. Bunescu,et al.  Subsequence Kernels for Relation Extraction , 2005, NIPS.

[7]  D. Roth 1 Global Inference for Entity and Relation Identification via a Linear Programming Formulation , 2007 .

[8]  L. Getoor,et al.  1 Global Inference for Entity and Relation Identification via a Linear Programming Formulation , 2007 .

[9]  Claudio Giuliano,et al.  Relation extraction and the influence of automatic named-entity recognition , 2007, TSLP.

[10]  Dan Roth,et al.  A Linear Programming Formulation for Global Inference in Natural Language Tasks , 2004, CoNLL.

[11]  Razvan C. Bunescu,et al.  A Shortest Path Dependency Kernel for Relation Extraction , 2005, HLT.

[12]  Rohit J. Kate A Dependency-based Word Subsequence Kernel , 2008, EMNLP.

[13]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[14]  Tom M. Mitchell,et al.  Coupling Semi-Supervised Learning of Categories and Relations , 2009, HLT-NAACL 2009.

[15]  Erik F. Tjong Kim Sang,et al.  Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition , 2003, CoNLL.

[16]  Alessandro Moschitti,et al.  Syntactic and Semantic Kernels for Short Text Pair Categorization , 2009, EACL.

[17]  Scott Miller,et al.  A Novel Use of Statistical Parsing to Extract Information from Text , 2000, ANLP.

[18]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[19]  Jun'ichi Tsujii,et al.  A Markov Logic Approach to Bio-Molecular Event Extraction , 2009, BioNLP@HLT-NAACL.

[20]  Dan Roth,et al.  The Use of Classifiers in Sequential Inference , 2001, NIPS.

[21]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[22]  Nello Cristianini,et al.  Classification using String Kernels , 2000 .

[23]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[24]  Jian Su,et al.  A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features , 2006, ACL.