Abstract Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source.
The WSI task was originally proposed as an alternative to overcome limitations imposed by systems that rely on sense inventories, which are manually created. The essential idea behind the WSI task is to group instances of words conveying the same meanings @cite_16 . In some studies, WSI methods are presented as unsupervised versions of the WSD task, particularly as an effort to overcome the knowledge acquisition bottleneck problem @cite_51 . Although some WSI methods have emerged along with the first studies on WSD, a comprehensive evaluation of methods was only possible with the emergence of shared tasks created specifically for the WSI task @cite_13 @cite_28 @cite_25 @cite_9 .
@cite_28 @cite_9 @cite_51 @cite_16 @cite_13 @cite_25
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The goal of this task is to allow for comparison across sense-induction and discrimination systems, and also to compare these systems to other supervised and knowledge-based systems. In total there were 6 participating systems. We reused the SemEval-2007 English lexical sample subtask of task 17, and set up both clustering-style unsupervised evaluation (using OntoNotes senses as gold-standard) and a supervised evaluation (using the part of the dataset for mapping). We provide a comparison to the results of the systems participating in the lexical sample subtask of task 17. Most work on word sense disambiguation has assumed that word usages are best labeled with a single sense. However, contextual ambiguity or fine-grained senses can potentially enable multiple sense interpretations of a usage. We present a new SemEval task for evaluating Word Sense Induction and Disambiguation systems in a setting where instances may be labeled with multiple senses, weighted by their applicability. Four teams submitted nine systems, which were evaluated in two settings. Word sense disambiguation has been recognized as a major problem in natural language processing research for over forty years. Both quantitive and qualitative methods have been tried, but much of this work has been stymied by difficulties in acquiring appropriate lexical resources. The availability of this testing and training material has enabled us to develop quantitative disambiguation methods that achieve 92 accuracy in discriminating between two very distinct senses of a noun. In the training phase, we collect a number of instances of each sense of the polysemous noun. Then in the testing phase, we are given a new instance of the noun, and are asked to assign the instance to one of the senses. We attempt to answer this question by comparing the context of the unknown instance with contexts of known instances using a Bayesian argument that has been applied successfully in related tasks such as author identification and information retrieval. The proposed method is probably most appropriate for those aspects of sense disambiguation that are closest to the information retrieval task. In particular, the proposed method was designed to disambiguate senses that are usually associated with different topics. Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed. In this paper we describe our Semeval-2013 task on Word Sense Induction and Disambiguation within an end-user application, namely Web search result clustering and diversification. Given a target query, induction and disambiguation systems are requested to cluster and diversify the search results returned by a search engine for that query. The task enables the end-to-end evaluation and comparison of systems. 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.