[PDF] Top 20 A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
Has 10000 "A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation" found on our website. Below are the top 20 most common "A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation".
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
6
A comparison of Named Entity Disambiguation and Word Sense Disambiguation
... connection between them may not be reli- able, calling for more evidence before the entity could be accepted as a possible referent for the ...as disambiguation pages, “list of” pages, and pages of dates — ... See full document
8
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
... for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular ...of Word Embeddings and Recurrent Neural Networks to design powerful context-related ... See full document
6
Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
... This comparison shows that the new examples have a radically different sense distribution than the SensEval ...similar sense distribution to the one in the test data is of ut- most ...for ... See full document
6
An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation
... learning algorithms (Mooney, 1996; Pedersen and Bruce, 1997) tend to base their comparison on only one word or at most a dozen ...two learning algorithms, k-nearest neighbor and ... See full document
8
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... 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 learned ... See full document
8
Domain Adaptation with Active Learning for Word Sense Disambiguation
... the sense priors in ...active learning to obtain the curve a-truePred in Fig- ure 3. For comparison, we also plot the curves a and a-truePrior for this set of 9 nouns in Figure ...compromise ... See full document
8
Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
... a sense to be the intended sense as identified by lexical and syntactic fea- tures is ...The sense which attains the highest score is chosen as the intended ...of comparison, in that there are ... See full document
8
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
... of sense- annotated data for different languages, a variety of multilingual preprocessing pipelines has also been developed across the years (Padr and Stanilovsky, 2012; Agerri et ...that supervised models ... See full document
6
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 possible ... See full document
6
Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... semi-supervised learning approaches, co-training and self-training are prob- ably the most ...of word sense ...for word sense ...semi-supervised learning techniques ... See full document
10
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi Supervised Learning Method
... Two main types of methods have been proposed to compensate for a lack of training data. One type is the semi-supervised learning method (Niu et al., 2005; Pham et al., 2005) or bootstrapping (Mihal- cea, ... See full document
10
Experiments on Active Learning for Croatian Word Sense Disambiguation
... hand, supervised approaches do not rely on lexi- cal resources and generally outperform knowledge- based approaches (Palmer et ...a sense-tagged corpus of 3200 words with 1000 instances per ... See full document
10
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
... semi-supervised learning model for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further improve accuracy over ... See full document
7
A Semi Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
... classification algorithms for di- mensionality reduction in feature ...art sense disambiguation ...outperforms supervised feature clustering and other dimensionality reduction ...than ... See full document
8
Learning Semantic Classes for Word Sense Disambiguation
... setting, supervised learning needs train- ing data created for each and every polysemous word; Ng (1997) estimates an effort of 16 person- years for acquiring training data for 3,200 significant ... See full document
8
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... a word has been rep- resented using a high-dimensional sparse Bag-of- Words (BoW) ...in word embeddings, where words meanings are represented by low-dimensional and dense vectors using deep ...With ... See full document
7
A New Minimally Supervised Framework for Domain Word Sense Disambiguation
... In this paper, we take domain WSD to the next level by proposing a new framework based on the minimally-supervised acquisition of large do- main sense inventories thanks to which high per- formance can be ... See full document
12
Learning Expressive Models for Word Sense Disambiguation
... using supervised learning (see Mihalcea et ...machine learning algorithms and attribute-value vectors to represent disambigu- ation ...the disambiguation models, that is, knowledge that ... See full document
8
Related subjects