Corpus ID: 5087222

The English all-words task

@inproceedings{Snyder2004TheEA,
  title={The English all-words task},
  author={Benjamin Snyder and Martha Palmer},
  booktitle={SENSEVAL@ACL},
  year={2004}
}
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wing.comp.nus.edu.sg
332 Citations
Highly Influential Citations
50
Background Citations
102
Methods Citations
98
Results Citations
16

332 Citations

Citation Type

Citation Type

Compression de vocabulaire de sens grâce aux relations sémantiques pour la désambiguïsation lexicale (Sense Vocabulary Compression through Semantic Knowledge for Word Sense Disambiguation)
TLDR
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A hybrid genetic-ant colony optimization algorithm for the word sense disambiguation problem
TLDR
This work proposes hybrid algorithms for WSD that consist of a self-adaptive genetic algorithm (SAGA) and variants of ant colony optimization (ACO) algorithms: max-min ant system (MMAS) and ant colony system (ACS). Expand
A graph-based approach to word sense disambiguation. An unsupervised method based on semantic relatedness
TLDR
A new method of combining similarity metrics that uses higher order relations between words to assign appropriate weights to each edge in the graph is introduced and a new approach for selecting the most appropriate sense of the target word that makes use of the in-degree centrality algorithm and senses of the neighbor words is proposed. Expand
Evaluating the word-expert approach for Named-Entity Disambiguation
TLDR
The results of the word-expert approach to NED are presented, where one classifier is built for each target entity mention string, as well as a study of the differences between WSD and NED, including ambiguity and synonymy statistics. Expand
Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser
TLDR
This work model the WSD problem as a Maximum A Posteriori (MAP) Inference Query on a Markov Random Field (MRF) built using WordNet and Link Parser or Stanford Parser, and their combination of dependency and MRF is novel. Expand
Coarse Word-Sense Disambiguation Using Common Sense
TLDR
This work has created a system for coarse word sense disambiguation using blending, a common sense reasoning technique, to combine information from SemCor, WordNet, ConceptNet and Extended WordNet to create a correct sense within that space. Expand
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance
TLDR
This paper presents a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. Expand
A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations
TLDR
The method proposed in this thesis attempts to address all words in unrestricted text based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. Expand
Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation
TLDR
QBERT is introduced, a Transformer-based architecture for contextualized embeddings which makes use of a co-attentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task. Expand
A clustering-based Approach for Unsupervised Word Sense Disambiguation
TLDR
An unsupervised disambiguation method relying on word sense clustering is presented that also reveals the implicit relationships (not asserted in WordNet) existing among these word senses. Expand
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References

SHOWING 1-2 OF 2 REFERENCES
English Tasks: All-Words and Verb Lexical Sample
TLDR
The experience in preparing the lexicon and sense-tagged corpora used in the English all-words and lexical sample tasks of Senseval-2 is described. Expand
WordNet : an electronic lexical database
TLDR
The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented. Expand