[PDF] Top 20 Extractive Summarization Using Supervised and Semi Supervised Learning
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Extractive Summarization Using Supervised and Semi Supervised Learning
... Supervised learning approaches normally achieve good performance but require manually labeled data. Recent literature (Blum and Mitchell, 1998; Collins, 199) has suggested that co-training techniques reduce ... See full document
8
Lγ-PageRank for semi-supervised learning
... Graph-based Semi-Supervised Learning (G-SSL) is a modern important tool for classi- ...Unsupervised Learning fully relies on the data structure and Supervised Learning demands ... See full document
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Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
... Co-training is a classic semi-supervised learning algorithm that can take advantage of unlabeled data to boost learning performance. Traditional co-training works under a two-view setting and ... See full document
10
Distribution-Based Semi-Supervised Learning for Activity Recognition
... Supervised learning methods have been widely applied to ac- tivity ...Distribution-based Semi-Supervised Learning, to tackle the aforementioned ...through semi-supervised ... See full document
8
Semi-Supervised Learning Using Greedy Max-Cut
... Graph-based semi-supervised learning (SSL) methods play an increasingly important role in prac- tical machine learning systems, particularly in agnostic settings when no parametric information ... See full document
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Sparse Semi-supervised Learning Using Conjugate Functions
... sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent target functions and thus has the merit of accelerating function ... See full document
33
Semi Supervised Learning for Relation Extraction
... This paper proposes a new effective and efficient semi-supervised learning method in relation ex- traction. First, a moderate number of weighted support vectors are bootstrapped from all the avail- ... See full document
8
Query focused Multi Document Summarization: Combining a Topic Model with Graph based Semi supervised Learning
... graph-based semi-supervised learning algorithms have been shown to be an effective way to impose a query’s influence on sentences (Zhou et al, 2003; Zhou et al, 2004; Wan et al, ... See full document
11
Towards Automated Semi-Supervised Learning
... Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, ...on supervised learning. In many applications, however, semi- ... See full document
8
Unbiased Generative Semi-Supervised Learning
... examines learning the parameters of both a single gaussian and a GMM when labels are ...on semi-supervised learning, in particular from the point of view of the Hughes phenomenon (Hughes, ... See full document
77
Semi-Supervised Learning with Measure Propagation
... graph-based semi-supervised learning based on minimizing the Kullback-Leibler divergence between discrete probability measures that encode class membership ...optimized using alternating ... See full document
60
Large Margin Semi-supervised Learning
... by using both unlabeled and labeled ...as semi-supervised learning, which differs from a conventional “missing data” problem in that the size of unlabeled data greatly exceeds that of labeled ... See full document
25
Semi-described and semi-supervised learning with Gaussian processes
... the outputs of the non-linear GP model. For this, we build on the variational approach of Titsias and Lawrence [2010] which allows for approximately propagating den- sities throughout the nodes of GP-based directed ... See full document
11
Self Supervised Learning for Contextualized Extractive Summarization
... for extractive summarization are usually trained from scratch with a cross- entropy loss, which does not explicitly cap- ture the global context at the document ... See full document
7
Using Supervised Bigram based ILP for Extractive Summarization
... competitive performance for extractive summa- rization task. Maximum marginal relevance (MMR) (Carbonell and Goldstein, 1998) uses a greedy algorithm to find summary sentences. (Mc- Donald, 2007) improved the MMR ... See full document
10
Cross-lingual sentiment classification using semi-supervised learning
... machine learning based ...machine learning methods train a sentiment classifier based on labelled data using some machine learning classification algorithms (Pang et ... See full document
53
Semi Supervised Learning of Concatenative Morphology
... We consider morphology learning in a semi-supervised setting, where a small set of linguistic gold standard analyses is available. We extend Morfessor Base- line, which is a method for unsupervised ... See full document
9
Semi Supervised Learning of Partial Cognates Using Bilingual Bootstrapping
... WSD is a task that has attracted researchers since 1950 and it is still a topic of high interest. Determining the sense of an ambiguous word, using bootstrapping and texts from a different language was done by ... See full document
8
A Review on health care examination records using data mining
... of learning the design for risk of unhealthy life in future lies in the unlabeled data which is a very integral part of the dataset which consist of the person’s data who is perfectly healthy and whose condition ... See full document
5
BottleSum: Unsupervised and Self supervised Sentence Summarization using the Information Bottleneck Principle
... for extractive summarization (Cheng and Lapata, ...sentence summarization (headline genera- tion). Thus, these supervised models do not gen- eralize well to other kinds of sentence summariza- ... See full document
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