[PDF] Top 20 Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
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Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... new semi-supervised learning model for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further ... See full document
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Chinese Word Sense Disambiguation based on Context Expansion
... approaches based on pseudo word have gained much attention in the NLP community (Yarowsky, 1995; Leacock et al, 1998; Mihalcea and Moldovan, 1999; Agirre and Marinez, 2004; Brody and Lapata, 2008; Lu et al, ... See full document
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Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
... the supervised training set by extracting additional examples from online lexicons and corpora, which are then annotated automatically with a WSD system based on seman- tic ...of supervised ... See full document
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Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... dresses supervised WSD with word embeddings is by Sugawara(Sugawara et ...vector based on context word embeddings (CWE) are merged, and they are used for training a classifier and ... See full document
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SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
... Word Sense Disambiguation (Navigli, 2009, WSD), is one of the long-standing challenges of Natural Language ...a word in context and a pre-specified sense inven- tory, the task of WSD is ... See full document
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Word Sense Disambiguation by Combining Labeled Data Expansion and Semi Supervised Learning Method
... -based semi-supervised WSD classifier is constructed by combining the discriminative and generative models trained on both labeled and unlabeled samples (See Fujino et ... See full document
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Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... different sense tags) will be added into classified data in each itera- tion till no such unlabeled examples can be ...1NN model was learned on this classified data and we used this model to perform ... See full document
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Semi supervised Word Sense Disambiguation with Neural Models
... all training sentences of the same ...language model will predict “night” for the input sentence “I fell asleep at ...the model to predict words that are close to one sense vector of the ... See full document
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Semi Supervised Preposition Sense Disambiguation using Multilingual Data
... learning word embeddings for use in a wide range of NLP tasks (Mikolov et ...target word, rather than representations of single ...2012), word sense dis- ambiguation (Chen et ...target ... See full document
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A Kernel PCA Method for Superior Word Sense Disambiguation
... in supervised word sense disambiguation (WSD) tasks without resort- ing to ad hoc voting or similar ensemble techniques has become somewhat daunting in recent years, given the challenging ... See full document
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A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
... This paper describes a set of comparative exper- iments, including cross-corpus evaluation, be- tween five alternative algorithms for supervised Word Sense Disambiguation WSD, namely Nai[r] ... See full document
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Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
... of word sense disambiguation based on word embeddings, su- pervised and ...of word embedding vectors. In place of ambiguous word we put embeddings specific for each ... See full document
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Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
... of supervised learning approaches to word sense disambiguation is largely de- pendent on the features used to represent the context in which an ambiguous word ...in ... See full document
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A New Minimally Supervised Framework for Domain Word Sense Disambiguation
... predominant sense of a target word by means of a semantic simi- larity measure (McCarthy et ...a word-category cooccur- rence matrix, where categories are coarse senses ob- tained from an existing ... See full document
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Knowledge Rich Word Sense Disambiguation Rivaling Supervised Systems
... in Word- Net and Wikipedia: the intersection between the two resources includes 80,295 lemmas which cor- respond to 105,797 WordNet senses and 199,735 Wikipedia ...WordNet sense for each page title (an ... See full document
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Semi Supervised Training for Statistical Word Alignment
... with Model 4 using “intersection” by look- ing at AER (with the “Sure” versus “Possible” link distinction), and restricted themselves to consider- ing 1-to-1 ... See full document
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A Unified Model for Word Sense Representation and Disambiguation
... Most word representation methods assume each word owns a single ...space model, where the contexts of each word are first clustered into groups, and then each cluster generates a distinct ... See full document
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Sequential Model Selection for Word Sense Disambiguation
... However, if the data sample can be adequately characterized by a less complex model, i.e., a model in which there are fewer interactions between variables, then more reliable parameter e[r] ... See full document
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Surrounding Word Sense Model for Japanese All-words Word Sense Disambiguation
... the sense distribution of the surrounding words of each target word before training using α : the transition probability param- ...the word senses in the ...possible word senses of the ... See full document
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Sense Embeddings in Knowledge Based Word Sense Disambiguation
... of sense embeddings creation and Lesk extension can be easily adapted to many language, requiring only a set of unannotated corpora, and a typical dictionary, thus, giving the possibility to create an efficient ... See full document
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