We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.
introduced AdaGram, a non-parametric method for learning sense embeddings based on a Bayesian extension of the Skip-gram model. The granularity of learned sense embeddings is controlled by the parameter @math . Comparisons of their approach to @cite_17 on three SemEval word sense induction and disambiguation datasets show the advantage of their method. For this reason, we use AdaGram as a representative of the state-of-the-art methods in our experiments.
@cite_17
2952854841 2251188465 2250962194 70010159
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task. Word Sense Induction (WSI) aims to automatically induce meanings of a polysemous word from unlabeled corpora. In this paper, we first propose a novel Bayesian parametric model to WSI. Unlike previous work, our research introduces a layer of hidden concepts and view senses as mixtures of concepts. We believe that concepts generalize the contexts, allowing the model to measure the sense similarity at a more general level. The Zipf’s law of meaning is used as a way of pre-setting the sense number for the parametric model. We further extend the parametric model to non-parametric model which not only simplifies the problem of model selection but also brings improved performance. We test our model on the benchmark datasets released by Semeval-2010 and Semeval-2007. The test results show that our model outperforms state-of-theart systems. Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013. This paper presents the description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems. In this task, participants were required to induce the senses of 100 target words using a training set, and then disambiguate unseen instances of the same words using the induced senses. Systems' answers were evaluated in: (1) an unsupervised manner by using two clustering evaluation measures, and (2) a supervised manner in a WSD task.