Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Introduction to the special issue on evaluating word sense disambiguation systems
Natural Language Engineering
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Combining unsupervised lexical knowledge methods for word sense disambiguation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
HLT '93 Proceedings of the workshop on Human Language Technology
Combining heterogeneous classifiers for word-sense disambiguation
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Building a sense tagged corpus with open mind word expert
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Consistent Validation of Manual and Automatic Sense Annotations with the Aid of Semantic Graphs
Computational Linguistics
Ensemble methods for unsupervised WSD
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Evaluating cross-language annotation transfer in the MultiSemCor corpus
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
PageRank on semantic networks, with application to word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Quality assessment of large scale knowledge resources
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Two graph-based algorithms for state-of-the-art WSD
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
SemEval-2007 task 07: coarse-grained English all-words task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Optimizing classifier performance in word sense disambiguation by redefining word sense classes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Text relatedness based on a word thesaurus
Journal of Artificial Intelligence Research
Knowledge-based sense disambiguation (almost) for all structures
Information Systems
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Incorporating coreference resolution into word sense disambiguation
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
ACM Transactions on Speech and Language Processing (TSLP)
An experimental study on unsupervised graph-based word sense disambiguation
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
The CQC algorithm: cycling in graphs to semantically enrich and enhance a bilingual dictionary
Journal of Artificial Intelligence Research
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
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The semantic annotation of texts with senses from a computational lexicon is a complex and often subjective task. As a matter of fact, the fine granularity of the WordNet sense inventory [Fellbaum, Christiane (ed.). 1998. WordNet: An Electronic Lexical Database MIT Press], a de facto standard within the research community, is one of the main causes of a low inter-tagger agreement ranging between 70% and 80% and the disappointing performance of automated fine-grained disambiguation systems (around 65% state of the art in the Senseval-3 English all-words task). In order to improve the performance of both manual and automated sense taggers, either we change the sense inventory (e.g. adopting a new dictionary or clustering WordNet senses) or we aim at resolving the disagreements between annotators by dealing with the fineness of sense distinctions. The former approach is not viable in the short term, as wide-coverage resources are not publicly available and no large-scale reliable clustering of WordNet senses has been released to date. The latter approach requires the ability to distinguish between subtle or misleading sense distinctions. In this paper, we propose the use of structural semantic interconnections – a specific kind of lexical chains – for the adjudication of disagreed sense assignments to words in context. The approach relies on the exploitation of the lexicon structure as a support to smooth possible divergencies between sense annotators and foster coherent choices. We perform a twofold experimental evaluation of the approach applied to manual annotations from the SemCor corpus, and automatic annotations from the Senseval-3 English all-words competition. Both sets of experiments and results are entirely novel: structural adjudication allows to improve the state-of-the-art performance in all-words disambiguation by 3.3 points (achieving a 68.5% F1-score) and attains figures around 80% precision and 60% recall in the adjudication of disagreements from human annotators.