[PDF] Top 20 Word Sense Induction using Cluster Ensemble
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Word Sense Induction using Cluster Ensemble
... ⎟ ⎥ ⎟ ⎦ ⎠ Re-parameterization is the process of constructing new features as combinations or transformations of the original features. We investigated Latent Semantic Indexing (LSI) method in our research and produce a ... See full document
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Overview of the Chinese Word Sense Induction Task at CLP2010
... teams using the k-means algorithm contain BUPT, LSTC, PKU1, DLUT and ...teams using the spectral clustering algorithm contain SCU and ...target word senses, traditional methods can achieve a good ... See full document
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Improved Estimation of Entropy for Evaluation of Word Sense Induction
... In this work, we analyzed the shortcomings of information-theoretic measures in the context of WSI evaluation and argued that main drawbacks of these approaches, such as the preference for the systems predicting richer ... See full document
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Improving Word Sense Induction by Exploiting Semantic Relevance
... Word Sense Induction (WSI) is the task of au- tomatically inducing the different senses of a target word from unannotated ...target word as a vector of selected features ...a ... See full document
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Chinese Word Sense Induction with Basic Clustering Algorithms
... of word senses are distributed within each cluster, while Purity measures the extent to which each cluster contained word senses from primarily one ...of cluster S r is defined as ... See full document
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One Million Sense Tagged Instances for Word Sense Disambiguation and Induction
... get word to represent the ...the sense of a tar- get word using positional word ...the word sense and topic ...one cluster, grouping each instance into a ... See full document
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A Sense Topic Model for Word Sense Induction with Unsupervised Data Enrichment
... Word sense induction (WSI) is the task of automat- ically discovering all senses of an ambiguous word in a ...ambiguous word with its surrounding ...a word indicates its ... See full document
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Latent Semantic Word Sense Induction and Disambiguation
... clustering, using the non-factorized dependency-based feature vectors (matrix ...the cluster is computed by averaging the fre- quencies of all cluster elements except for the tar- get word we ... See full document
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Triplet Based Chinese Word Sense Induction
... Sense induction is typically treated as a clustering problem, by considering their co- occurring contexts, the instances of a target word are partitioned into ...get word is ... See full document
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Word Sense Induction Using Lexical Chain based Hypergraph Model
... Word Sense Induction is a task of automatically finding word senses from large scale ...target word occurs and hyperedges represent high- er-order semantic relatedness among ...finding ... See full document
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Word Sense Induction & Disambiguation Using Hierarchical Random Graphs
... target word and ver- tices with a low ...to cluster the graph and pro- duce a set of clusters (senses) each one consisting of a set of contextually related ... See full document
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Class based Word Sense Induction for dot type nominals
... annotated sense assigned to its corresponding in- duced sense cluster (literal, metonymic or under- ...underspecified sense to cluster with the induced sense that contains a ... See full document
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Evaluating Unsupervised Ensembles when applied to Word Sense Induction
... of Word Sense Induction with a framework for combining diverse feature spaces and cluster- ing ...existing Word Sense In- duction ... See full document
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Chinese Word Sense Induction based on Hierarchical Clustering Algorithm
... A feature set is used designed to capture both immediate local context in our experiment, wider context and syntactic context. Specifically, we experimented with several feature categories: ±5-word window (5w), ... See full document
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Naive Bayes Word Sense Induction
... Bayesian WSI systems have been developed by several authors. Brody and Lapata (2009) apply Latent Dirichlet Allocation (LDA) (Blei et al., 2003) to WSI. They run a topic modeling algorithm on texts with some fixed number ... See full document
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Using Pseudowords for Algorithm Comparison: An Evaluation Framework for Graph based Word Sense Induction
... as a simplified version of it and similarly simulates the flow of information in a graph. Initially, every node in the graph starts as a member of its own class; then, at each iteration every node assumes the prevalent ... See full document
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Combining Lexical Substitutes in Neural Word Sense Induction
... methods cluster word ego-networks consisting of a single node (ego) together with the nodes they are connected to (alters) and all the edges among those ...target word or context features relevant to ... See full document
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Word Sense Induction with Neural biLM and Symmetric Patterns
... Hard-clustering of representatives Let V be the vocabulary obtained from all the representa- tives. We associate each representative with a sparse |V | dimensional bag-of-features vector, and arrange the representatives ... See full document
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AutoSense Model for Word Sense Induction
... of sense clusters us- ing cluster error, which is the mean absolute error between the detected number and the actual number of sense ...the cluster errors of LDA (Blei, Ng, and Jordan 2003), ... See full document
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Measuring the Impact of Sense Similarity on Word Sense Induction
... Spectral Clustering Spectral Clustering inter- prets a dataset’s elements as vertices in graph with edges based on their similarity (Ng et al., 2001). Clusters are found by identifying the graph parti- tion that produces ... See full document
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