[PDF] Top 20 A Semi Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
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A Semi Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
... Supervised feature clustering algorithm groups features into clusters based on the distribution of class labels over ...un- supervised feature clustering method can not ... See full document
8
PageRank on Semantic Networks, with Application to Word Sense Disambiguation
... link-analysis algorithm (Brin and Page, 1998), and variants like Kleinberg’s HITS algorithm (Kleinberg, 1999), have been used for an- alyzing the link-structure of the World Wide Web to provide global, ... See full document
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Knowledge Rich Word Sense Disambiguation Rivaling Supervised Systems
... The use of collaborative contributions from vol- unteers has been previously shown to be beneficial in the Open Mind Word Expert project (Chklovski and Mihalcea, 2002). However, its current status indicates that ... See full document
10
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance
... coarse-grained sense distinc- tions and show that they help boost disambigua- tion on standard test sets? We believe that this is a crucial research topic in the field of WSD, that could potentially benefit ... See full document
8
Enriching Wordnet for Word Sense Disambiguation
... lexicalizes). Application-specific inventories can also be ...treat word translations as word senses, an approach that is becoming increasingly feasible because of the availability of large ... See full document
6
Semi supervised Word Sense Disambiguation with Neural Models
... Word sense disambiguation (WSD) is a long-standing problem in natural language processing (NLP) with broad ...applications. Supervised, unsupervised, and knowledge-based approaches have been ... See full document
12
A New Minimally Supervised Framework for Domain Word Sense Disambiguation
... These results were obtained in a fully unsuper- vised setting in which no structured knowledge was provided, unlike previous applications of PPR to WSD (Agirre et al., 2009; Agirre and Soroa, 2009) which relied on the ... See full document
12
Supervised Approach to Word Sense Disambiguation
... developed in Indian languages like Hindi, Malayalam, Manipuri, Nepali, Kannada but no such automated system has yet emerged for the Indo-Aryan language- Assamese. Their future work aims to develop a model for the WSD ... See full document
6
Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... The nature of KPCA, however, suggests a strategy that is not applicable to many of the other conventional WSD models. We propose a model in this paper that takes ad- vantage of unsupervised training using large ... See full document
7
A Review on Word Sense Disambiguation
... whereas supervised and clustering methods learn from example ...the supervised and the clustering methods is that training data is not required for each word that needs to be ... See full document
6
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi Supervised Learning Method
... Many words have multiple meanings that change depending on the context. Recently, it has been confirmed that word sense disambiguation (WSD) improves certain NLP applications such as parse selection ... See full document
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A Semi Supervised Method for Arabic Word Sense Disambiguation Using a Weighted Directed Graph
... Lesk algorithm is limited to dictionary defini- tions that we ...Lesk algorithm using the Leacock and Chodorow measure (Leacock and Chodorow, 1998) is the most performed between based knowledge methods with ... See full document
5
Semi Supervised Preposition Sense Disambiguation using Multilingual Data
... Note that unlike previous experiments, adding external word embeddings improves the context model over the base model significantly, approaching the results of the multilingual model. For this reason, we also ... See full document
12
An Overview of Semi-Supervised Fuzzy Clustering Algorithms
... Fuzzy clustering is a group of algorithms for clustering analysis, in which the data elements are distributed to the cluster is not “clear” (elements belong to only one cluster) that are “fuzzy” in the ... See full document
6
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... used semi-supervised learning method for WSD, bootstrapping algorithm works by iteratively classifying unlabeled examples and adding confidently classified examples into labeled dataset using a model ... See full document
8
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
... target word in a given context is a problem that is commonly referred to as Word Sense Disambiguation ...the semi-supervised setting (Taghipour and Ng, 2015b; Yuan et ... See full document
6
Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
... The supervised baseline system The supervised system uses memory-based learn- ing, in the implementation of the Tilburg Memory- Based Learner (TiMBL) ... See full document
6
Semi supervised Relation Extraction with Large scale Word Clustering
... using word clusters as features in discriminative learning was pioneered by Miller et ...hierarchical word clusters generated by the Brown clustering algorithm (Brown et ...the word ... See full document
9
Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... of word embed- dings and the adaptation ...of word embeddings lead to improvements on lexical sample ...CW word embeddings over the base- line are significant (p < ...adapted word ... See full document
10
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical Semantic Combinations
... The rationale behind the creation of such re- sources was substantiated in a knowledge-based WSD study conducted by Navigli and Lapata (2010), who hypothesized an improvement in per- formance by several points when ... See full document
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