The logic of typed feature structures
The logic of typed feature structures
C4.5: programs for machine learning
C4.5: programs for machine learning
The graph isomorphism problem: its structural complexity
The graph isomorphism problem: its structural complexity
Machine Learning
Average-case computational complexity theory
Complexity theory retrospective II
Meaning and grammar (2nd ed.): an introduction to semantics
Meaning and grammar (2nd ed.): an introduction to semantics
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic labeling of semantic roles
Computational Linguistics
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
The TREC question answering track
Natural Language Engineering
Applied morphological processing of English
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Hierarchical directed acyclic graph kernel: methods for structured natural language data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic learning of textual entailments with cross-pair similarities
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discovering asymmetric entailment relations between verbs using selectional preferences
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Satisfying information needs with multi-document summaries
Information Processing and Management: an International Journal
Tree kernels for semantic role labeling
Computational Linguistics
Overview of the Answer Validation Exercise 2007
Advances in Multilingual and Multimodal Information Retrieval
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Recognizing textual entailment using a subsequence kernel method
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A machine learning approach to textual entailment recognition
Natural Language Engineering
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Experimenting a "General purpose" textual entailment learner in AVE
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Building structures from classifiers for passage reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
One of the most important research area in Natural Language Processing concerns the modeling of semantics expressed in text. Since foundational work in Natural Language Understanding has shown that a deep semantic approach is still not feasible, current research is focused on shallow methods combining linguistic models and machine learning techniques. The latter aim at learning semantic models, like those that can detect the entailment between the meaning of two text fragments, by means of training examples described by specific features. These are rather difficult to design since there is no linguistic model that can effectively encode the lexico-syntactic level of a sentence and its corresponding semantic models. Thus, the adopted solution consists in exhaustively describing training examples by means of all possible combinations of sentence words and syntactic information. The latter, typically expressed as parse trees of text fragments, is often encoded in the learning process using graph algorithms. In this paper, we propose a class of graphs, the tripartite directed acyclic graphs (tDAGs), which can be efficiently used to design algorithms for graph kernels for semantic natural language tasks involving sentence pairs. These model the matching between two pairs of syntactic trees in terms of all possible graph fragments. Interestingly, since tDAGs encode the association between identical or similar words (i.e. variables), it can be used to represent and learn first-order rules, i.e. rules describable by first-order logic. We prove that our matching function is a valid kernel and we empirically show that, although its evaluation is still exponential in the worst case, it is extremely efficient and more accurate than the previously proposed kernels.