[PDF] Top 20 Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
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Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
... on domain information as its main knowledge ...same domain; (ii) polysemy reduction, because the potential am- biguity of terms is sensibly lower if the domain of the text is specified; and (iii) ... See full document
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On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation
... an unsupervised system to learn the predominant senses of particular ...the domain corpus and ...supervised domain adaptation on a manually selected subset of 21 nouns from the DSO ...predominant ... See full document
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Domain Kernels for Word Sense Disambiguation
... On the other hand, the word expert approach works very well for lexical sample WSD tasks (i.e. tasks in which it is required to disambiguate only those words for which enough training data is pro- vided). As the ... See full document
8
Enriching Wordnet for Word Sense Disambiguation
... linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word ...the word has ... See full document
6
Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
... We trained a standard statistical phrase-based English-German translation system from the re- sources described above using Moses (Hoang and Koehn, 2008). Individual language models were trained for each data source and ... See full document
9
A New Minimally Supervised Framework for Domain Word Sense Disambiguation
... These results were obtained in a fully unsuper- vised setting in which no structured knowledge was provided, unlike previous applications of PPR to WSD (Agirre et al., 2009; Agirre and Soroa, 2009) which relied on the ... See full document
12
Unsupervised Word Sense Disambiguation Using Neighborhood Knowledge
... the word collection of a sentence, the semantic similarity between two sentences relies on word’s ...a word is represented by a synonym ...evaluate word semantic similarity based on the co-occurrence ... See full document
10
Co-occurrence graphs for word sense disambiguation in the biomedical domain
... and unsupervised methods when applied to the NLM dataset, and even semi-supervised ...our disambiguation phase is completely based on the co-occurrence graph created from the abstracts, so it does not need ... See full document
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Estimating Class Priors in Domain Adaptation for Word Sense Disambiguation
... evaluation on the nouns of SENSEVAL-2 English lexical sample task (Kilgarriff, 2001). In another recent evaluation on the nouns of SENSEVAL- 2 English all-words task (Chan and Ng, 2005a), promising results were also ... See full document
8
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation
... and it was not study that improved a classifier made from supervised learning by using topic model. Cai’s paper described above, a method that the topic features are added to the normal fea- tures was implemented as a ... See full document
8
An Unsupervised Approach to Chinese Word Sense Disambiguation Based on Hownet
... a word and calculates the importance of the context to depict the word, so that the precision position of the word in vector space can be ...the word sequence in the context is ignored by ... See full document
10
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
... Each sense cluster is automatically labeled to improve its ...hypernym relevance score, calculated as P w∈cluster sim(t, w)freq(w, h), and the hypernym coverage score, calculated as P w∈cluster min(freq(w, ... See full document
13
A Review on Word Sense Disambiguation
... Unsupervised learning learns how machines can be trained to signify specific input patterns in a means that imitate the statistical structure of the inclusive collection of input ...Since unsupervised ... See full document
6
Unsupervised Domain Tuning to Improve Word Sense Disambiguation
... invariant sense distribution for each topic, p(w, s|t). Once this word sense distribution is obtained, the underlying WSD algorithm is never needed ...correct sense within an individual text ... See full document
5
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
8
Subjectivity Word Sense Disambiguation
... (Hovy et al., 2006)). We also have evidence that a moderate amount of manual annotation would be worth the effort. For example, let us order the lexi- con entries from highest to lowest by frequency in the MPQA corpus. ... See full document
10
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
... We followed the WSD process described in Sec- tion 2 and 3 using the WordNet 2.1 sense repository that is adopted by SemEval-2007 Task 07. All exper- iments were performed on a Pentium 2.33GHz dual core PC with ... See full document
9
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
... a disambiguation vocabu- lary by extracting words from the WordNet lex- ical knowledge base, as ...the disambiguation vocabulary words indicated by specific WordNet semantic re- lations that depend on the ... See full document
11
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation
... Results of the experiments are shown in Table. 2. As the result, relationships, Base-M < Base-S , Mtd-G-M < Mtd-G-S, Mtd-L-M < Mtd-L-S, Ours-G-M < Ours-G-S, and Ours-L-M < Ours-L-S are satisfied. It is ... See full document
9
Ant Colony Algorithm for the Unsupervised Word Sense Disambiguation of Texts: Comparison and Evaluation
... two unsupervised algorithms from the state of the art, a Genetic Algorithm (GA) (Gelbukh et ...other unsupervised algorithms, using the Semeval-2007, Task-7, Coarse grained corpus (Navigli et ... See full document
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