[PDF] Top 20 Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models
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Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models
... context of most concepts, like the ones shown, can be easily understood. For example, the first concept is about government actions and the second deals with murder and accidental deaths. The penulti- mate concept is ... See full document
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Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation
... by using the similarity (Van Asch and Daelemans, ...by using the property 1 including the distance between domains (Komiya and Okumura, 2012) (Komiya and Okumura, ... See full document
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Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
... above using Moses (Hoang and Koehn, 2008). Individual language models were trained for each data source and were then lin- early interpolated with weights optimized on the development ... See full document
9
It Takes Two to Tango: A Bilingual Unsupervised Approach for Estimating Sense Distributions using Expectation Maximization
... for sense annotated corpora without compromising on ...acquired sense labels for the source ...of bilingual parallel cor- pora may be an unreasonable demand for many language pairs (perhaps more ... See full document
10
The Noisy Channel Model for Unsupervised Word Sense Disambiguation
... a probabilistic generative model for word sense disambiguation that seamlessly integrates unlabeled text data into the model building ...for unsupervised word sense ... See full document
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Unsupervised Relation Discovery with Sense Disambiguation
... possible sense per ...into sense clusters using local and global ...these sense clus- ters into semantic relations using hierarchical agglomerative ...than models without ... See full document
9
Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
... an unsupervised TC technique, has been proposed and evaluated in- side the Domain Driven Disambiguation frame- work, showing a significant improvement on the overall system ...clear probabilistic ... See full document
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Unsupervised, Knowledge Free, and Interpretable Word Sense Disambiguation
... word sense is represented as a cluster of words. Next, the induced sense inventory is used as a pivot to generate sense representations by aggregation of the context clues of cluster ...the ... See full document
6
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
... visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, ...OntoNotes sense realized in the ...perform unsupervised visual sense ... See full document
11
Building Specialized Bilingual Lexicons Using Word Sense Disambiguation
... the bilingual dictionary and all monosemic words appearing whithin the same context ...on models of distribu- tional similarity learned from large text collec- ... See full document
5
Sense Extraction and Disambiguation for Chinese Words from Bilingual Terminology Bank
... one sense, a monosemous word, it can be tagged with that sense ...one sense, we should use a disambiguation method to get the appropriate ...word sense disambiguation (WSD) ... See full document
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Unsupervised Word Sense Disambiguation Using Bilingual Comparable Corpora
... An unsupervised method for word sense disam- biguation using a bilingual comparable corpus was ...the sense that maxi- mizes the score, ...each sense and the clues appearing in ... See full document
7
Models and Training for Unsupervised Preposition Sense Disambiguation
... improve disambiguation of the words linked by the prepositions (here, morn- ing, shopped, and ...propose using un- supervised methods in order to leverage unlabeled data, since, to our knowledge, there are ... See full document
6
An Unsupervised Word Sense Disambiguation System for Under Resourced Languages
... Each word sense disambiguation method extends the BaseWSD class. This class provides the end user with a generic interface for WSD and also encapsulates com- mon routines for data pre-processing. The ... See full document
5
Unsupervised Domain Tuning to Improve Word Sense Disambiguation
... Using a Wilcoxon signed-rank test, the results were found to be significantly better over the orig- inal algorithms in every case (apart from Topic- Words). Both the WordNet similarity (sim) and the VSM approach ... See full document
5
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
Unsupervised document zone identification using probabilistic graphical models
... learning, using widely known classifiers such as Naive Bayes (Teufel and Moens, 2002), Hidden Markov Model (Li et ...approaches using active learning have only started to gain attention very recently (Guo ... See full document
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
... an unsupervised do- main adaptation for Word Sense Disambigua- tion (WSD) using Stacked Denoising Autoen- coder ...an unsupervised learn- ing method of obtaining the abstract feature set of ... See full document
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Unsupervised All words Word Sense Disambiguation with Grammatical Dependencies
... Because of data sparseness, there may be not enough evidence in the corpus to produce a clear winner, and several senses are tied. All senses are then kept, and disambiguation proceeds further. If more than one ... See full document
6
Learning Expressive Models for Word Sense Disambiguation
... Models learned with ILP are symbolic and can be easily interpreted. Additionally, innovative knowl- edge about the problem can emerge from the rules learned by the system. Although some rules simply test shallow ... See full document
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