Data sparsity
Addressing the Data Sparsity Issue in Neural AMR Parsing
... the Data Sparsity Issue While sequence-to-sequence models can be suc- cessfully applied to constituent parsing, they do not work well on the AMR parsing task as shown by Barzdins and Gosko ...training ...
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Grey Forecast Model for Accurate Recommendation to Cope with Data Sparsity and Correlation
... Despite its widespread adoption, memory based CF still suffers from several major problems including the data sparsity problem [1][8], data correlation problem [5], and cold start problem [6][7]. The ...
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Grey forecast model for accurate recommendation in presence of data sparsity and correlation
... the data is sparse or when there are strong data correlations, UCF and ICF methods do not perform well when it comes to prediction ...movie data sets, namely, MovieLens and ...of data ...
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Accounting for data sparsity when forming spatially coherent zones
... with data sparsity. Data sparsity can impact a dataset in different ways, through variable sparsity, spatial sparsity or colocation sparsity ...Variable sparsity ...
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On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint
... RKHS, sparsity in the dual space representation is equivalent to sparsity in the kernel ...proposed data sparsity constraint is able to provide such a sparse representation for general loss ...
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Accounting for data sparsity when forming spatially coherent zones
... Spatial sparsity occurs when data are not collected uniformly across a field, this is the case for many infield ...spatial sparsity generates holes in the coverage of data over a field and as ...
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Generalising and Normalising Distributional Contexts to Reduce Data Sparsity: Application to Medical Corpora
... In this paper, we address the reduction of data sparsity in matrices of context vectors used to imple- ment the distributional analysis. We proposed to generalise and normalise the distributional contexts ...
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Grey Forecast Model for Accurate Recommendation in Presence of Data Sparsity and Correlation
... Grey theory was originally developed by Deng in 1982 [22]. It mainly focuses on model uncertainty and information insufficiency in analyzing and understanding systems via research on conditional analysis, prediction and ...
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Three phase training to address data sparsity in Neural Machine Translation
... Data sparsity is a challenging problem in NMT, especially for resource-scarce language ...of data sparsity in NMT, using only little amount of parallel ...
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Understanding the impact of data sparsity and duration for location prediction applications
... location data has be- come commonplace, enabling the creation of models of behaviour that support location ...of data used in location prediction must be un- derstood in order to optimise storage and ...
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Solving Data Sparsity by Morphology Injection in Factored SMT
... SMT approaches suffer due to data sparsity while translating into a morphologically rich language. We solve this problem by enriching the origi- nal data with the missing morphological forms of ...
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Distantly Supervised POS Tagging of Low Resource Languages under Extreme Data Sparsity: The Case of Hittite
... the data sparsity caused by multilingualism and rich Hittite mor- phology poses additional challenges for statisti- cal NLP ...training data for a POS ...
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On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter
... Twitter data from six different datasets (obtained from the litera- ture of Twitter sentiment classification) and observe how removing stopwords affects two well-known supervised sen- timent classification ...
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Decreasing Lexical Data Sparsity in Statistical Syntactic Parsing Experiments with Named Entities
... lexical data sparsity in parsers have been important ever since the lexical- isation of parsers led to significant improvements in parser performance (Collins, 1999; Charniak, ...the data over more ...
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Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity
... We use this architecture for the network because of the constraint on the dataset size caused by scarcity of labelled data. We used a NVIDIA 970 GTX GPU and a 4.00 GHz Intel i7-4790 proces- sor with 64GB RAM to ...
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Quantile Regression Based on Laplacian Manifold Regularizer with the Data Sparsity in l1 Spaces
... . Meanwhile, the quantile regression provides a piecewice linearity but a convex technique to overcome difficulties such as a high nonlinearity dependence on the predictor and linear suboptimal models. Finally, the ...
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On reducing the data sparsity in collaborative filtering recommender systems
... Regression-based latent factor models, which was proposed by Agarwal et al. [96], has also been widely used for collaborative filtering. It integrates attributes of both users and items with U-I preference data ...
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Reducing the Impact of Data Sparsity in Statistical Machine Translation
... n-best output using RNNLM which has also been shown to be helpful by (Liu et al., 2014). Shi (2012) also showed the benefits of using RNNLM with contextual and linguistic features. Follow- ing their work, we used three ...
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Accounting for data sparsity when forming spatially coherent zones
... These are illustrative examples of a “bad” (jagged, ill-behaved curve), “moderate” and “good” (smooth, with clear maximum identifiable) smoothing assessments, respectively... A)-B) The f[r] ...
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GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model
... the data sparsity ...the data sparsity of LDA and the strong assumption of BTM, we sample a fixed number of documents to form a mini-corpus as a training ...
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