Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Machine learning with lexical features: the Duluth approach to Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Distinguishing easy and hard instances
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Natural Language Engineering
Case-Sensitivity of Classifiers for WSD: Complex Systems Disambiguate Tough Words Better
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Making sense of word sense variation
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
Paraphrase identification using machine learning techniques
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
Anveshan: a framework for analysis of multiple annotators' labeling behavior
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations
Language Resources and Evaluation
| Hi-index | 0.00 |
This paper presents a comparative evaluation among the systems that participated in the Spanish and English lexical sample tasks of SENSEVAL-2. The focus is on pairwise comparisons among systems to assess the degree to which they agree, and on measuring the difficulty of the test instances included in these tasks.