BabelNet: building a very large multilingual semantic network
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Knowledge-rich Word Sense Disambiguation rivaling supervised systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Human action recognition in video by 'meaningful' poses
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Modeling sense disambiguation of human pose: recognizing action at a distance by key poses
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Ontologizing concept maps using graph theory
Proceedings of the 2011 ACM Symposium on Applied Computing
Unsupervised word sense disambiguation with lexical chains and graph-based context formalization
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Towards open ontology learning and filtering
Information Systems
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Graph-based named entity linking with wikipedia
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Recognizing interaction between human performers using 'key pose doublet'
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
ACM Transactions on Speech and Language Processing (TSLP)
Automatically structuring domain knowledge from text: An overview of current research
Information Processing and Management: an International Journal
BabelNetXplorer: a platform for multilingual lexical knowledge base access and exploration
Proceedings of the 21st international conference companion on World Wide Web
A quick tour of word sense disambiguation, induction and related approaches
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
The CQC algorithm: cycling in graphs to semantically enrich and enhance a bilingual dictionary
Journal of Artificial Intelligence Research
Multilingual WSD with just a few lines of code: the BabelNet API
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Joining forces pays off: multilingual joint word sense disambiguation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Exploring dictionary-based semantic relatedness in labeled tree data
Information Sciences: an International Journal
Collaboratively built semi-structured content and Artificial Intelligence: The story so far
Artificial Intelligence
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
TaxoLearn: A Semantic Approach to Domain Taxonomy Learning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
International Journal of Web Engineering and Technology
Cognitive canonicalization of natural language queries using semantic strata
ACM Transactions on Speech and Language Processing (TSLP)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Utilizing BDI Agents and a Topological Theory for Mining Online Social Networks
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most “important” node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.