[PDF] Top 20 Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
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Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... of semi-supervised learning approaches, co-training and self-training are prob- ably the most ...samples using one (self-training) or two (co-training) classifiers, trained on a smaller set of ... See full document
10
Semi supervised Word Sense Disambiguation with Neural Models
... held-out word given the surrounding context, with a large amount of unlabeled text as training ...dimensional embeddings), which achieves high precision without ...the word senses and it is trained ... See full document
12
Semi-Supervised Multi-Task Word Embeddings
... learned semi- supervised multi-task learning (SS-MTL) embeddings to analogy ...the word meta- embeddings carry over to analogy even if not all embedding algorithms preserve analogy ... See full document
9
Zero shot Word Sense Disambiguation using Sense Definition Embeddings
... definition embeddings. Both the non-neural and neu- ral supervised baselines presented here rely on a back-off mechanism, using WordNet S1 for words unseen during ...all supervised and ... See full document
12
Embeddings for Word Sense Disambiguation: An Evaluation Study
... of word embed- dings in the framework of ...First, word embeddings can be used as new features to improve a state-of-the-art supervised WSD that only uses standard ...integrating ... See full document
11
A Comparison of Word Embeddings for English and Cross Lingual Chinese Word Sense Disambiguation
... that supervised approaches have performed best in WSD, we focus on inte- grating word embeddings in supervised approaches; in specific, we explore the use of word ... See full document
10
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) ...of specific in- stances but not for all ... See full document
6
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi Supervised Learning Method
... in general, they are beaten by supervised approaches if train- ing data are provided (Tanaka et ...SemEval-2010, supervised approaches achieved the best results (Murata et ...target word and ... See full document
10
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 embeddings and computes log probability from sequences of word embedding ...ambiguous ... See full document
6
Parameter Transfer across Domains for Word Sense Disambiguation
... problem; supervised and knowledge-based meth- ods rely on predefined sense inventories (Agirre and Edmonds, 2007; Mart´ınez et ...include sense induction and discrimination (Nav- igli, 2012; Brody et ... See full document
8
A New Minimally Supervised Framework for Domain Word Sense Disambiguation
... main sense inventories thanks to which high per- formance can be attained on virtually any domain using unsupervised ...eral domains with minimal supervision and without the requirement of ... See full document
12
Sense Embeddings in Knowledge Based Word Sense Disambiguation
... (NLP), Word Sense Disambiguation (WSD) aims at assigning the most probable sense of a word in a document, given a pre-defined sense ...often supervised systems, as stated ... See full document
7
Multi sense Embeddings through a Word Sense Disambiguation Process
... as: word order is lost, data sparsity and high dimensionality, to name a ...faster general sin- gle convolutional network architecture showing that multitask and semi-supervised learning can ... See full document
15
Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... target word whose context contains a combination of features that may in fact be interdependent, but are not similar to any combinations that occurred in the limited amounts of labeled training ...get word ... See full document
7
Enriching Wordnet for Word Sense Disambiguation
... a sense s of a word w can radically change the overlap result, as glosses form the primary ingredient to the sense ...appropriate sense of the concept in question using a Lesk like ... 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
Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings
... namely word sense er- rors, our approach differs in that we pair a human reference translation not just with one contrastive example, but a set of contrastive examples, ...identify specific model ... See full document
9
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... dresses supervised WSD with word embeddings is by Sugawara(Sugawara et ...context word embeddings (CWE) are merged, and they are used for training a classifier and ...the word in ... See full document
7
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
... given that both implementations are written in Java, we tested their programmatic usage within a Java program. We relied on a testing corpus with 1M words and more than 250K target instances to disambiguate, and we used ... See full document
6
Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora
... for word sense disambiguation using a bilin- gual comparable corpus together with sense definitions by translations into another language was ...mous word and the clues, i.e., ... See full document
7
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