Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The disambiguation of nominalizations
Computational Linguistics
Corpus statistics meet the noun compound: some empirical results
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Natural Language Engineering
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
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A semantic scattering model for the automatic interpretation of genitives
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Automatically learning qualia structures from the web
DeepLA '05 Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition
Extraction of semantic word relations in Turkish from dictionary definitions
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
| Hi-index | 0.00 |
This paper addresses the problem of the identification of the semantic relations in Italian complex nominals (CNs) of the type N+P+N. We exploit the fact that the semantic relation, which is underspecified in most cases, is partially made explicit by the preposition. We develop an annotation framework around five different semantic relations, which we use to create a corpus of 1700 Italian CNs, obtaining an inter-annotator agreement of K=.695. Exploiting this data, for each preposition p we train a classifier to assign one of the five semantic relations to any CN of the type N+p+N, by using both string and supersense features. To obtain supersenses, we experiment with a sequential tagger as well as a plain lookup in MultiWordNet, and find that using information obtained from the former yields better results.