![]() We do not need to know the sense/meaning of each cluster, but sentences inside a cluster should have used the target words with the same sense. The problem states as: Given a target word (e.g., “cold”) and a collection of sentences (e.g., “I caught a cold”, “The weather is cold”) that use the word, cluster the sentences according to their different senses/meanings. Word sense induction (WSI) is widely known as the “unsupervised version” of WSD. ![]() Evaluation metrics are as same as All words task. Main tasks include Senseval 2, Senseval 3 and SemEval 2007. (2016) provide the state-of-the-art results until 2016. In this task a number of words are selected and the system should only disambiguate the occurrences of these words in a test set. ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension WSD Lexical Sample task:Ībove task is called All-words WSD because the systems attempt to disambiguate all of the words in a document, while there is another task which is called Improved Word Sense Disambiguation with Enhanced Sense Representations ESC: Redesigning WSD with Extractive Sense Comprehension With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders Word Sense Disambiguation: A Comprehensive Knowledge Exploitation Framework Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation Knowledge-based Word Sense Disambiguation using Topic Models Random walks for knowledge-based word sense disambiguation Entity Linking meets Word Sense Disambiguation: A Unified Approach Embeddings for Word Sense Disambiguation: An Evaluation Study It makes sense: A wide-coverage word sense disambiguation system for free text Incorporating Glosses into Neural Word Sense Disambiguation Deep contextualized word representations context2vec: Learning generic context embedding with bidirectional lstm Neural Sequence Learning Models for Word Sense Disambiguation Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison The scores of and are not taken from the original papers but from the results of the implementations of and, respectively. Note: ‘All’ is the concatenation of all datasets, as described in and. ![]() The first sense given by the underlying sense inventory (i.e. Knowledge-based: Knowledge-based systems usually exploit WordNet or BabelNet as semantic network. 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. ![]() Some supervised methods, particularly neural architectures, usually employ the SemEval 2007 dataset as development set (marked by *). All supervised systems in the evaluation table are trained on SemCor. Supervised: The most widely used training corpus used is SemCor, with 226,036 sense annotations from 352 documents manually annotated. 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). 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. The Evaluation framework of Raganato et al. We would assign “mouse” with its electronic device sense ( the 4th sense in the WordNet sense inventory). “A mouse consists of an object held in one’s hand, with one or more buttons.” The de-facto sense inventory for English in WSD is WordNet.įor example, given the word “mouse” and the following sentence: The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory.
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