[PDF] Top 20 Sprinkling Topics for Weakly Supervised Text Classification
Has 10000 "Sprinkling Topics for Weakly Supervised Text Classification" found on our website. Below are the top 20 most common "Sprinkling Topics for Weakly Supervised Text Classification".
Sprinkling Topics for Weakly Supervised Text Classification
... semi- supervised text classification algorithms with the aim of reducing the time, effort and cost involved in labeling ...Semi-supervised text classification algorithms proposed ... See full document
6
Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords
... Although a fair amount of literature on hedging in scientific texts has been produced since the 1990s (e.g. (Hyland, 1994)), speculative language from a Natural Language Processing perspective has only been studied in ... See full document
9
Weakly Supervised Cross lingual Semantic Relation Classification via Knowledge Distillation
... Finally, our training procedure uses knowledge distillation (Hinton et al., 2014) to alleviate the lack of annotated cross-lingual pairs. Knowl- edge distillation has been proposed to compress a model with many ... See full document
12
A Variational Approach to Weakly Supervised Document Level Multi Aspect Sentiment Classification
... the text content describ- ing a particular aspect is generated by sampling words from a topic model corresponding to the latent ...a weakly supervised text classification method which ... See full document
11
Improved Arabic Dialect Classification with Social Media Data
... dialect classification methods solely rely on textural features, be they n-gram language model or morphology/POS-based fea- ...a weakly supervised dialect classifier. Using the weakly and ... See full document
9
Identifying Opinion Topics and Polarity of Parliamentary Debate Motions
... the topics mentioned and opinions expressed in parliamentary debate motions– or proposals–is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent ... See full document
6
Semi Supervised Representation Learning for Cross Lingual Text Classification
... sentiment classification on the parallel training and test ...lingual text classification based on non-negative ma- trix ...latent topics as interlingual rep- resentations (Mimno et ... See full document
11
Weakly Supervised Approaches for Ontology Population
... Automatic Ontology Population (OP) from texts has recently emerged as a new field of application for knowledge acquisition techniques (see, among others, (Buitelaar et al., 2005)). Although there is no a univocally ... See full document
8
Weakly-Supervised Hierarchical Text Classification
... a weakly-supervised hierarchical text classifi- cation method ...various supervised and weakly-supervised baselines in three datasets from different domains, which demonstrates ... See full document
8
Weakly Supervised Tweet Stance Classification by Relational Bootstrapping
... stance classification, in such do- mains as Congressional debates and online forums, has been a topic of interest in the past ...from text classification to structured output predic- tion, including ... See full document
6
Weakly Supervised Learning for Hedge Classification in Scientific Literature
... Recent years have witnessed a significant growth of research into weakly supervised ML techniques for NLP applications. Different approaches are of- ten characterised as either multi- or single-view, where ... See full document
8
Learning a Deep Hybrid Model for Semi Supervised Text Classification
... For text-based classification, a dominating model is the support vector machine (SVM) (Cortes and Vapnik, 1995) with many useful in- novations to yet further improve its discrimina- tive performance ... See full document
11
A Semi Supervised Approach for Catchphrase Classification in Legal Text Documents
... To determine which parts of the text, which can be removed without loss of relevant information for output and this is done through the filter. Content is less important to the summary, such as units of martyrdom ... See full document
11
Supervised classification of end of lines in clinical text with no manual annotation
... This situation is mentioned by some authors, e.g., by Miller et al. (2015) for the MIMIC II clin- ical texts, or by Zweigenbaum & Grouin (2014) for the i2b2/UTHealth 2014 NLP challenge docu- ments (Stubbs et al., ... See full document
9
Automatic Assessment of Medication States of Patients with Parkinson’s Disease using Wearable Sensors
... signals, text, or other type of ...a classification or regression model is dimensionality reduction that includes feature extraction and ...the classification or regression ...for supervised ... See full document
101
Weakly supervised evidence pinpointing and description
... is weakly-supervised requiring only the pathological labels and no other prior ...additional weakly annotated data, which gives robust localisation with average error within 2 mm and ... See full document
13
Weakly Supervised Training of Semantic Parsers
... several weakly su- pervised relation extractors (Craven and Kumlien, 1999; Mintz et ...extends weakly supervised rela- tion extraction to produce richer semantic structure, using only slightly more ... See full document
12
Volume 2, Issue 3, March 2013 Page 90
... and classification algorithms were ...well-known classification algorithms: Naive Bayesian (NB) approach, Rocchio’s-style classifier, k-NN method and SVM ... See full document
10
Automatic Prediction and Patient Stratification Using Multi Objective Evolutionary Classification and Clustering Algorithm Using WEKA Tools
... in Text classification by ...classifier classification doesn't alter if documents that don't belong to the support vectors area unit off from the set of coaching ... See full document
9
Active Semi-supervised Framework with Data Editing
... labeled text classification aggravate such ...real-world text data sets shows the encouraging results of the proposed framework for sparsely labeled text classification, compared with ... See full document
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