WordSenseInduction

In this paper a novel solution to auto- matic and unsupervised word sense induc- tion (WSI) is introduced. It represents an instantiation of the ‘one sense per colloca- tion’ observation …...

Word Sense Disambiguation is the task dedi- cated to the problem of finding out the sense of a word in context, from all of its many pos- sible senses. Solving …...

A single word may have multiple un- specified meanings in a corpus. Word sense induction aims to discover these dif- ferent meanings through word use, and knowledge-lean algorithms attempt this …...

In this paper, we present a novel approach to Web search result clustering based on the au- tomatic discovery of word senses from raw text, a task referred to as …...

Word sense induction and discrimination (WSID) identifies the senses of an am- biguous word and assigns instances of this word to one of these senses. We have build a WSID …...

. Lexical-semantic resources are fundamental building blocks in natural language processing (NLP). Frequently, they fail to cover the informal vocabulary of web users as represented in user-generated content. This paper …...

In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automati- cally extracted …...

We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically de- tect words with …...

The Duluth systems that participated in task 11 of SemEval–2013 carried out word sense induction (WSI) in order to cluster Web search results. They relied on an approach that repre- …...

We describe the LIMSI system for the SemEval-2013 Cross-lingual Word Sense Dis- ambiguation (CLWSD) task. Word senses are represented by means of translation clusters in different languages built by a …...

We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the …...

This paper describes our system for Task 11 of SemEval-2013. In the task, participants are provided with a set of ambiguous search queries and the snippets returned by a search …...

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 …...

Word sense induction is an unsupervised task to find and characterize different senses of polyse- mous words. This work investigates two unsupervised approaches that focus on using distribu- tional word …...

Word Sense Induction is a task of automatically finding word senses from large scale texts. It is general- ly considered as an unsupervised clustering problem. This paper introduces a hypergraph …...

AbstractWord sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and …...

This paper describes the Duluth systems that participated in Task 15 of SemEval 2015. The goal of the task was to automatically construct dictionary entries (via a series of three …...

Word embedding has been widely studied and proven helpful in solving many natural lan- guage processing tasks. However, the ambi- guity of natural language is always a prob- lem on …...

AbstractWord sense induction (WSI) seeks to induce senses of words from unannotated corpora. In this paper, we address the WSI task for the Croatian language. We adopt the word clustering …...

Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or co- occurrence features, and train a model for each polysemous word individually. In this work, …...

AbstractWe propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction. Our approach jointly models global lexical, local lexical and dependency syntactic …...

AbstractThe current trend in NLP is the use of highly opaque models, e.g. neural networks and word embeddings. While these models yield state-of-the-art results on a range of tasks, their …...

AbstractThis paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such …...

Standard word embeddings lack the possibility to distinguish senses of a word by projecting them to exactly one vector. This has a negative effect particularly when computing similarity scores between …...

AbstractWord Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by …...

AbstractLexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone …...

AbstractSemEval-2020 Task 1 is devoted to detection of changes in word meaning over time. The first subtask raises a question if a particular word has acquired or lost any of …...

ABSINTH1 provides a novel unsupervised graph-based approach to word sense induction. This work combines small world co¨occurrence networks with a graph propagation algorithm to induce per-word sense assignment vectors over …...

AbstractWe present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is …...