Word Sense Disambiguation
The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
The Evaluation framework of Raganato et al. 2017  includes two training sets (SemCor-Miller et al., 1993- and OMSTI-Taghipour and Ng, 2015-) and five test sets from the Senseval/SemEval series (Edmonds and Cotton, 2001; Snyder and Palmer, 2004; Pradhan et al., 2007; Navigli et al., 2013; Moro and Navigli, 2015), standardized to the same format and sense inventory (i.e. WordNet 3.0).
Typically, there are two kinds of approach for WSD: supervised (which make use of sense-annotated training data) and knowledge-based (which make use of the properties of lexical resources).
Supervised: The most widely used training corpus used is SemCor, with 226,036 sense annotations from 352 documents manually annotated. All supervised systems in the evaluation table are trained on SemCor. Some supervised methods, particularly neural architectures, usually employ the SemEval 2007 dataset as development set (marked by *). The most usual baseline is the Most Frequent Sense (MFS) heuristic, which selects for each target word the most frequent sense in the training data.
Knowledge-based: Knowledge-based systems usually exploit WordNet or BabelNet as semantic network. The first sense given by the underlying sense inventory (i.e. WordNet 3.0) is included as a baseline.
The main evaluation measure is F1-score.
|Model||Senseval 2||Senseval 3||SemEval 2007||SemEval 2013||SemEval 2015||Paper / Source|
|Model||All||Senseval 2||Senseval 3||SemEval 2007||SemEval 2013||SemEval 2015||Paper / Source|
|WN 1st sense baseline||65.2||66.8||66.2||55.2||63.0||67.8|||
Note: 'All' is the concatenation of all datasets, as described in  and . The scores of [6,7] and  are not taken from the original papers but from the results of the implementations of  and , respectively.
 Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison
 Neural Sequence Learning Models for Word Sense Disambiguation
 context2vec: Learning generic context embedding with bidirectional lstm
 Deep contextualized word representations
 Incorporating Glosses into Neural Word Sense Disambiguation
 It makes sense: A wide-coverage word sense disambiguation system for free text
 Embeddings for Word Sense Disambiguation: An Evaluation Study
 Entity Linking meets Word Sense Disambiguation: A Unified Approach
 Random walks for knowledge-based word sense disambiguation
 Knowledge-based Word Sense Disambiguation using Topic Models
 SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
 The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD