[PDF] Top 20 Co training and Self training for Word Sense Disambiguation
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Co training and Self training for Word Sense Disambiguation
... of co-training consists in the re- lation between the views used in ...of co-training, (Blum and Mitchell, 1998) state conditional independence of the views as a required cri- terion for ... See full document
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Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further improve accuracy over supervised KPCA models that have achieved WSD ... See full document
7
Learning of word sense disambiguation rules by Co-training, checking co-occurrence of features
... improve Co-training and apply it to word sense disambiguation ...problems. Co-training is an unsupervised learning method to overcome the problem that labeled ... See full document
5
Embedding Senses for Efficient Graph based Word Sense Disambiguation
... high-quality word vector repre- sentations trained on large collections of ...of word-forms extracted from the corpus used to train the ...a word, predict its context; the training algorithm, ... See full document
5
Co-occurrence graphs for word sense disambiguation in the biomedical domain
... Regardless of whether we refer to general or specific domains, such as the biomedical one, it is commonly accepted in the literature [5, 6, 1] that most WSD algorithms fall into one of the fol- lowing categories: ... See full document
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Huge Automatically Extracted Training Sets for Multilingual Word SenseDisambiguation
... Word Sense Disambiguation is a crucial task in Natural Language Processing as it can be beneficial to several downstream applications, ...”word sense” is and choos- ing a corresponding ... See full document
5
Determining the Difficulty of Word Sense Disambiguation
... labeled training data while the unsupervised ones rely on the UMLS ...difficult disambiguation will be for ambiguous terms against the output of two WSD ... See full document
9
A Review on Word Sense Disambiguation
... that training data is not required for each word that needs to be ...all-words disambiguation. All-words disambiguation methods have an advantage over what is termed lexical-sample ... See full document
6
A comparison of Named Entity Disambiguation and Word Sense Disambiguation
... potential training data for NED in the form of human-generated anchor-texts, while hand-annotated data for WSD is a scarce ...Frequent Sense baseline, a widely used WSD ...the sense frequency ... See full document
8
A word sense disambiguation corpus for Urdu
... proper sense of a word in a particular ...for training and testing models based on features extracted using WSD ...one word from the left, and one word from right side of target ... See full document
22
Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
... obtain training data for Subjectivity Word Sense Disambiguation (SWSD) as described in (Akkaya et ...which word instances in a corpus are being used with sub- jective senses, and which ... See full document
9
Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora
... We evaluated our method through an experiment using corpora of English and Japanese newspaper articles. The first language was English and the second lan- guage was Japanese. A Wall Street Journal corpus (July, 1994 to ... See full document
7
Training Word Sense Embeddings With Lexicon based Regularization
... of training prediction-based word em- bedding models, much of the research into obtain- ing word sense representations revolved around ...context-based word sense ... See full document
11
Word Sense Disambiguation with Multilingual Features
... predominant sense encountered in the training ...frequent sense baseline is often times difficult to surpass because many of the words exhibit a disproportionate usage of their main sense ... See full document
10
Enriching Wordnet for Word Sense Disambiguation
... treat word translations as word senses, an approach that is becoming increasingly feasible because of the availability of large multilingual parallel corpora that can serve as training ...data. ... See full document
6
Domain Kernels for Word Sense Disambiguation
... the word expert approach works very well for lexical sample WSD tasks ...enough training data is pro- ...of training data, exploiting instead ex- ternal knowledge acquired in an unsupervised way to ... See full document
8
Word Sense Disambiguation by Relative Selection
... target word in a sentence by selecting only a relative among the relatives of the target word that most probably occurs in the ...target word, a set of relatives of the target word is created ... See full document
13
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... We used three types of features to capture con- textual information: part-of-speech of neighboring words with position information, unordered sin- gle words in topical context, and local collocations (as same as the ... See full document
8
Models and Training for Unsupervised Preposition Sense Disambiguation
... improve disambiguation of the words linked by the prepositions (here, morn- ing, shopped, and ...joint disambiguation of preposition and arguments in a future ... See full document
6
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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