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BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network
We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedicExpand
Word sense disambiguation: A survey
This work introduces the reader to the motivations for solving the ambiguity of words and provides a description of the task, and overviews supervised, unsupervised, and knowledge-based approaches. Expand
Entity Linking meets Word Sense Disambiguation: a Unified Approach
Babelfy is presented, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Expand
BabelNet: Building a Very Large Multilingual Semantic Network
A very large, wide-coverage multilingual semantic network that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia and Machine Translation is also applied to enrich the resource with lexical information for all languages. Expand
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
A unified evaluation framework is developed and the results show that supervised systems clearly outperform knowledge-based models in Word Sense Disambiguation, and a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Expand
Neural Sequence Learning Models for Word Sense Disambiguation
This work proposes and studies in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models, and shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features. Expand
SemEval-2007 Task 10: English Lexical Substitution Task
The English Lexical Substitution task for SemEval is described, in the task, annotators and systems find an alternative substitute word or phrase for a target word in context that involves both finding the synonyms and disambiguating the context. Expand
Embeddings for Word Sense Disambiguation: An Evaluation Study
This work proposes different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and performs a deep analysis of how different parameters affect performance. Expand
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
This paper introduces a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training, and investigates several measures of graph connectivity with the aim of identifying those best suited for WSD. Expand
Structural semantic interconnections: a knowledge-based approach to word sense disambiguation
  • R. Navigli, P. Velardi
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 July 2005
Structural semantic interconnections (SSI) is presented, which creates structural specifications of the possible senses for each word in a context and selects the best hypothesis according to a grammar G, describing relations between sense specifications. Expand