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SemEval

Known as: Senseval, Word Sense Induction and Disambiguation task, Multilingual and Crosslingual WSD 
SemEval (Semantic Evaluation) is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense… 
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Related topics

Related topics

26 relations
Automatic summarizationBabelNetClassic monolingual word-sense disambiguationComputational semantics

Broader (2)

Computational linguisticsNatural language processing

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing
We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our… 
2015
2015
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings
The present paper reports on the results of automatic noun compound interpretation for English using a deep neural network… 
2014
2014
An Assessment of Online Semantic Annotators for the Keyword Extraction Task
The task of keyword extraction aims at capturing expressions (or entities) that best represent the main topics of a document… 
2008
2008
Genetic Word Sense Disambiguation Algorithm
A novel unsupervised genetic word sense disambiguation (GWSD) algorithm is proposed in this paper. The algorithm first uses… 
2006
2006
Word Relatives in Context for Word Sense Disambiguation
The current situation for Word Sense Disambiguation (WSD) is somewhat stuck due to lack of training data. We present in this… 
Highly Cited
2005
Highly Cited
2005
SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text
This paper describes SENSELEARNER --- a minimally supervised word sense disambiguation system that attempts to disambiguate all… 
2002
2002
SENSEVAL: The evaluation of word sense disambiguation systems
Word sense disambiguation (WSD) is the problem of deciding which sense a word has in any given context. The problem of doing WSD… 
Highly Cited
2000
Highly Cited
2000
Memory-Based Word Sense Disambiguation
We describe a memory-based classification architecture for word sense disambiguation and its application to the SENSEVAL… 
Review
2000
Review
2000
Peeling an Onion: The Lexicographer's Experience ofManual Sense-Tagging
SENSEVAL set itself the task of evaluating automaticword sense disambiguation programs (see Kilgarriff andRosenzweig, this volume… 
2000
2000
A Topical/Local Classifier for Word Sense Identification
TLC is a supervised training (S) system that uses a Bayesianstatistical model and features of a word's context to identifyword… 
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