[PDF] Top 20 Stopping Criteria for Active Learning of Named Entity Recognition
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Stopping Criteria for Active Learning of Named Entity Recognition
... The table shows that class probabilities are in fact estimated too optimistically. For many of the entries in the positives table, the estimated prob- abilities are greater than the empirical probabili- ties. In the ... See full document
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Active learning for ontological event extraction incorporating named entity recognition and unknown word handling
... novel active learning method for ontological event extraction, which is more complex than the simple PPI ...of named entity recognition into the active learning for event ... See full document
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Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities
... an active learning solution for named entity recognition, attempting to maxi- mize a custom model’s improvement per addi- tional unit of manual ...quality named entity ... See full document
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On Proper Unit Selection in Active Learning: Co Selection Effects for Named Entity Recognition
... Active learning is an effective method for cre- ating training sets cheaply, but it is a biased sampling process and fails to explore large regions of the instance space in many appli- ...down ... See full document
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An active learning-enabled annotation system for clinical named entity recognition
... Although the results in this user study showed that the current AL methods could not be guaranteed to save annotation time, compared to passive learning, we gained valuable information about why it happened. If ... See full document
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Multi Criteria based Active Learning for Named Entity Recognition
... • Strategy 1 : We first consider the informative- ness criterion. We choose m examples with the most informativeness score from NESet to an in- termediate set called INTERSet. By this pre- selecting, we make the ... See full document
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Deep Active Learning for Named Entity Recognition
... Over the past several years, a series of papers have used deep neural networks (DNNs) to advance the state of the art in named entity recognition (NER) (Collobert et al., 2011; Huang et al., 2015; ... See full document
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Proactive Learning for Named Entity Recognition
... proactive learning has been proposed to model dif- ferent types of experts (Donmez and Carbonell, 2008, ...Proactive learning assumes that (1) not all annotators are perfect, but that there is at least one ... See full document
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Active Learning with Amazon Mechanical Turk
... AL has been successfully applied to a number of NLP tasks such as part-of-speech tagging (Ringger et al., 2007), parsing (Osborne and Baldridge, 2004), text classification (Tong and Koller, 2002), senti- ment detection ... See full document
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A Semi-supervised Learning Approach to Arabic Named Entity Recognition
... This paper introduces ASemiNER, an Arabic semi-supervised NER system built under minimal supervision. Gazetteers (predefined lists of NEs) and annotated corpora are not required by ASem- iNER. That is, ASemiNER is a ... See full document
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“Discriminative Learning with Hybridised framework for Obtaining the Named Entity Recognition”
... monthly active users, and given the fact that more than 500 million tweets are sent per day, there lies a money for information extraction researchers and it attract attention of academics and organizations to get ... See full document
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A Joint Named Entity Recognition and Entity Linking System
... As outlined in section 1.2, the input for the stan- dard EL task consists in sets of entity mentions from a number of documents, sent as queries to a linking system. Our current task differs in that we aim at both ... See full document
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Learning Dictionaries for Named Entity Recognition using Minimal Supervision
... since named entities often contain more than one ...by learning a binary SVM using the CCA embeddings of a high recall, low precision list of candidate phrases to predict whether a candidate phrase is a ... See full document
10
Learning Multilingual Meta Embeddings for Code Switching Named Entity Recognition
... Implementation Details Our model is trained using a Noam optimizer with a dropout of 0.1 for multilingual setting and 0.3 for the cross- lingual setting. Our model contains four lay- ers of transformer blocks with a ... See full document
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Aggregating Machine Learning and Rule Based Heuristics for Named Entity Recognition
... for Named Entity Recognition for South and South East Asian ...machine learning techniques with language specific heuris- tics to model the problem of NER for In- dian ...machine ... See full document
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Cross lingual Transfer Learning for Japanese Named Entity Recognition
... We present experimental results on external, i.e. publicly available, corpora, as well as on internally gathered large-scale real-world datasets. First, a deep neural network model is developed for NER, and we ... See full document
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Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition
... In this work we have introduced hierarchical feature tree priors for use in transfer learning on named en- tity extraction tasks. We have provided evidence that motivates these models on intuitive, ... See full document
9
Named Entity Recognition and Classification for Entity Extraction
... machine learning techniques required for annotating datasets for training the ...machine learning approach is to be done with R programming which is a powerful language for data ... See full document
5
Distantly Supervised Named Entity Recognition using Positive Unlabeled Learning
... (PU) learning problem and accordingly introduce a novel PU learning algorithm to perform the ...labeled entity words form the positive (P) data and the rest form the unlabeled (U) data for PU ... See full document
11
A Comparative Review of Machine Learning for Arabic Named Entity Recognition
... Zirikly and Diab [25] proposed dialectal Arabic NER system using Egyptian colloquial Arabic. Their machine- learning approach uses CRF approach to recognizing persons and locations NEs. They used NER features, ... See full document
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