[PDF] Top 20 Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
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Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
... The algorithm uses these properties to incrementally identify collocations for target senses of a word, given a few seed collocations 1Note that the problem here is sense disambiguation:[r] ... See full document
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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
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Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
... a supervised WSD module is not an option without collecting addi- tional ...a word sense distinction is often not lo- cated in the immediately surrounding context but it is either at a more distant ... See full document
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A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
... which sense of a ho- mograph is used in a given ...WSD methods use the context of a word for its sense disambiguation, and the context informa- tion can come from either ... See full document
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Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
... these sense repre- sentations, adding to them links to external re- sources, such as Wikipedia, topical category la- bels, and images representing the ...un- supervised models listed above do not feature ... See full document
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Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... using word embeddings instead of a thesaurus can improve the accuracy of ...using word embeddings instead of a thesaurus can improve the accuracy of WSD because of following reasons; there are a lot of ... See full document
7
Knowledge Rich Word Sense Disambiguation Rivaling Supervised Systems
... highest-performing supervised ones in a coarse-grained setting and to outperform them on domain-specific ...enrich Word- Net with a large amount of semantic edges, the method can be reversed and applied to ... See full document
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Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... These methods randomly select a subset of a large unlabeled dataset and classify these samples using one (self-training) or two (co-training) classifiers, trained on a smaller set of labeled ...these ... See full document
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An Unsupervised Approach to Chinese Word Sense Disambiguation Based on Hownet
... Section 2 describes our method. In section 2.1, we introduce some related information about Hownet. Section 2.2 describes the method for establishing second-order contexts and the method for clustering these context ... See full document
10
An Unsupervised Word Sense Disambiguation System for Under Resourced Languages
... Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a ...for word sense ...an ... See full document
5
Unsupervised All words Word Sense Disambiguation with Grammatical Dependencies
... Column Random ( Rand. ) shows a simple ran- dom baseline, and column Sequence (Seq.) shows the sequence data labelling method (Mihalcea, 2005) – one of the best performing graph-methods (Navigli and Lapata, 2007). ... See full document
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Combining Unsupervised and Supervised Methods for PP Attachment Disambiguation
... We started by computing the cooccurrence values over word forms for nouns, preposi- tions, and verbs based on their part-of-speech tags. In order to compute the pair frequen- cies f req(N 1 , P ), we search the ... See full document
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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 ... See full document
12
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
... Figure 3: An example of XML parser. class and implement the parse method. An example is provided by the PlainParser class that implements a parser for a plain textual file. Adding a new preprocessing module. To add a new ... See full document
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Unsupervised Word Sense Disambiguation Using Neighborhood Knowledge
... graph-based methods for WSD have gained much attention in the NLP community (Veronis, 2004, Sinha and Mihalcea, 2007, Navigli and Lapata, 2007, Mihalcea, 2005, Agirre E, ...These methods have been proposed ... See full document
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Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation
... learning methods for each target word by using the property 1 including the distance between domains (Komiya and Okumura, 2012) (Komiya and Okumura, ... See full document
8
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... manually sense-tagged data is an obstacle to supervised word sense dis- ambiguation ...semi- supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning ... See full document
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Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
... data. Unsupervised example sentences (Fig 1(a)) cause improvements that overcome the results of Ta- ble ...new word types, or expand the number of senses covered by the already existing classifiers (Fig ... See full document
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Ant Colony Algorithm for the Unsupervised Word Sense Disambiguation of Texts: Comparison and Evaluation
... other methods. In this paper, we focus on three methods that globally propagate a local algorithm based on semantic relatedness to the span of a whole ...two unsupervised algorithms from the state of ... See full document
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
... classifier using the traing data from source domain; and then, classify the test data from target domain by the classifier (as represented by source tar- get ). There are six domain adaptation patterns: (1) PB OC, (2) OC ... See full document
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