In LMMS has two useful properties: 1) uses contextual particular, it focuses on polysemous words which word embeddings to produce sense embeddings, have been hard to represent as embeddings due and 2) covers a large set of over 117K to the meaning conflation deficiency (Camacho-senses from WordNet 3.0. The first property allows Collados and Pilehvar, 2018). The task's objective for comparing precomputed sense embeddings is to detect if target words occurring in a pair of against contextual word embeddings generated sentences carry the same meaning.

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.

This paper explores the application of knowledge-based Word Sense Disambiguation systems to specific domains, based on our state-of-the-art graph-based WSD system that uses the information in WordNet. Evaluation was performed over a publicly available domain-specific dataset of 41 words related to Sports and Finance, comprising examples drawn from three corpora: one balanced corpus (BNC), and two domain-specific corpora (news related to Sports and Finance). The results show that in all three corpora our knowledge-based WSD algorithm improves over previous results, and also over two state-of-the-art supervised WSD systems trained on SemCor, the largest publicly available annotated corpus. We also show that using related words as context, instead of the actual occurrence contexts, yields better results on the domain datasets, but not on the general one.  Interestingly, the results are higher for domain-specific corpus than for the general corpus, raising prospects for improving current WSD systems when applied to specific domains.