Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear concepts and hidden variables
Machine Learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Intricacies of Collins' Parsing Model
Computational Linguistics
Exploring evidence for shallow parsing
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Use of deep linguistic features for the recognition and labeling of semantic arguments
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Demonstrating an interactive semantic role labeling system
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
A pipeline framework for dependency parsing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A hybrid convolution tree kernel for semantic role labeling
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
/*icomment: bugs or bad comments?*/
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
A global joint model for semantic role labeling
Computational Linguistics
Labeling chinese predicates with semantic roles
Computational Linguistics
Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling
ACM Transactions on Asian Language Information Processing (TALIP)
Identifying semitic roots: Machine learning with linguistic constraints
Computational Linguistics
Search-based structured prediction
Machine Learning
Semantic Tree Kernels to classify Predicate Argument Structures
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A MDL-based model of gender knowledge acquisition
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Baby SRL: modeling early language acquisition
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Minimally supervised model of early language acquisition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Competitive generative models with structure learning for NLP classification tasks
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transliteration as constrained optimization
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Accurate parsing of the proposition bank
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
An inference model for semantic entailment in natural language
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Dependency parsing with second-order feature maps and annotated semantic information
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Automatic event-level textual emotion sensing using mutual action histogram between entities
Expert Systems with Applications: An International Journal
Lexical and structural biases for function parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
A Robust Geometric Model for Argument Classification
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
An Information-Extraction System for Urdu---A Resource-Poor Language
ACM Transactions on Asian Language Information Processing (TALIP)
PKU_HIT: An event detection system based on instances expansion and rich syntactic features
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Building an end-to-end text reading system based on a packed representation
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
A unified framework for scope learning via simplified shallow semantic parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Improving the quality of text understanding by delaying ambiguity resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Learning the scope of negation via shallow semantic parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Combining constituent and dependency syntactic views for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Tree kernel-based semantic role labeling with enriched parse tree structure
Information Processing and Management: an International Journal
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
On amortizing inference cost for structured prediction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
We provide an experimental study of the role of syntactic parsing in semantic role labeling. Our conclusions demonstrate that syntactic parse information is clearly most relevant in the very first stage - the pruning stage. In addition, the quality of the pruning stage cannot be determined solely based on its recall and precision. Instead it depends on the characteristics of the output candidates that make downstream problems easier or harder. Motivated by this observation, we suggest an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves the performance.