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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
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Word sense disambiguation: A survey
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is atExpand
  • 1,625
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Entity Linking meets Word Sense Disambiguation: a Unified Approach
Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL theExpand
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BabelNet: Building a Very Large Multilingual Semantic Network
In this paper we present BabelNet -- a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic andExpand
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An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
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 thisExpand
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SemEval-2007 Task 10: English Lexical Substitution Task
In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The taskExpand
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Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
Word Sense Disambiguation is a longstanding task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic,Expand
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Embeddings for Word Sense Disambiguation: An Evaluation Study
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, manyExpand
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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
Word sense disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as WebExpand
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Neural Sequence Learning Models for Word Sense Disambiguation
Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, whichExpand
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