[PDF] Top 20 Unsupervised Domain Tuning to Improve Word Sense Disambiguation
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Unsupervised Domain Tuning to Improve Word Sense Disambiguation
... incorrect) sense to all occurrences of the word tie, while both PPR based algorithms detect an obvious domain ...each word appear in Semcor), while sim does not get enough information from the ... See full document
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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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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
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Domain Adaptation with Active Learning for Word Sense Disambiguation
... form domain adaptation of WSD systems to new do- ...in sense priors (i.e., the proportions of the different senses of a word) between BC and ...the sense pri- ors of each word in BC and ... See full document
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ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
... for word sense disam- biguation (WSD) at the document ...likely sense configurations for each ...local sense config- urations are assembled into longer com- posite configurations based on ... See full document
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Unsupervised, Knowledge Free, and Interpretable Word Sense Disambiguation
... Second, word senses are induced by clustering of an ego-network of related words (Biemann, ...discovered word sense is represented as a cluster of ...induced sense inventory is used as a pivot ... See full document
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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
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A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
... coarse-grained sense inventory cre- ated with SSI algorithm (Navigli and Velardi, 2005), training data, and test ...special tuning, neither coarse-grained sense inventory nor training data was ... See full document
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Ant Colony Algorithm for the Unsupervised Word Sense Disambiguation of Texts: Comparison and Evaluation
... Word Sense Disambiguation (WSD) is a core problem in Natural Language Processing (NLP), as it may improve many of its applications, such as multilingual information extraction, automatic ... 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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Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
... to improve previous ...2.85 sense definitions per word on av- ...3.62 sense definitions per word on ...or unsupervised) and the data ... See full document
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Estimating Class Priors in Domain Adaptation for Word Sense Disambiguation
... in sense priors (i.e., the proportions of the different senses of a word) between BC and ...corpus: sense 1, 2, 3, 4, 5, and ...the sense priors of each word in BC and WSJ, and adjusted ... See full document
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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
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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
Contextual Modeling for Meeting Translation Using Unsupervised Word Sense Disambiguation
... stricted by role assignments. In the first case, the scenario was a project meeting about the devel- opment of a new TV remote control; participant roles were project manager, industrial designer, marketing expert, etc. ... See full document
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Unsupervised Word Sense Disambiguation with Multilingual Representations
... an unsupervised method based on the Lesk algorithm (Lesk, ...of word senses that maximizes the redundancy (overlap) across all corresponding ...of word senses, while still find- ing an optimal ... See full document
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
... In our experiment, we have used three domains: Yahoo! Answers (OC), Books (PB), and newspa- per (PN) from the Balanced Corpus of Contempo- rary Written Japanese (Maekawa, 2007), along with 16 selected ambiguous words. ... See full document
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Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
... good domain relevance measure for sev- eral ...actual sense (i.e. the sense in which they are used in the context) in advance, especially in a WSD ... See full document
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Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
... Each sense cluster is automatically labeled to improve its ...cluster word w to the target word t, and the freq(w, h) is the frequency of the hypernymy relation (w, h) as ex- tracted via ... See full document
13
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
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