[PDF] Top 20 Crowdsourcing Semantic Label Propagation in Relation Classification
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Crowdsourcing Semantic Label Propagation in Relation Classification
... performing relation extraction from text that is known to produce noisy ...in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and ... See full document
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A Unified Architecture for Semantic Role Labeling and Relation Classification
... Semantic relation identification and classification are important problems towards the understanding of natural language ...Multi-typed semantic relations have been defined between two terms ... See full document
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An Iterative Approach for Joint Dependency Parsing and Semantic Role Labeling
... dependency relation labeling and semantic role ...build classification models for every ...dependency relation labeling) is added to the iterative step, it is useful to add it to the iterative ... See full document
6
Two View Label Propagation to Semi supervised Reader Emotion Classification
... emotion classification. However, the classification performance greatly suffers when the size of the la- beled data is ...two-view label propagation approach to semi-supervised reader emotion ... See full document
9
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
... discourse relation classification lies in extracting relevant information for the relation label from (the combination of) the discourse relational ... See full document
12
Relation Extraction Using Label Propagation Based Semi Supervised Learning
... Pattern Relation Expan- sion) (Brin, 1998) is a bootstrapping-based sys- tem that used a pattern matching system as clas- sifier to exploit the duality between sets of pat- terns and ...approaches relation ... See full document
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Combining Relational and Attributional Similarity for Semantic Relation Classification
... disambiguation, semantic role labeling, and tex- tual entailment are already well-established and are gradually finding their way in real NLP ap- plications, while a number of new semantic tasks are ... See full document
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Task Oriented Learning of Word Embeddings for Semantic Relation Classification
... spect which n-grams are relevant to each relation class after the supervised learning step of RelEmb. When the context size c is 3, we can use at most 7-grams. The learned weight matrix S in Sec- tion 3.3 is used ... See full document
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Multi Label Text Classification through Label Propagation
... object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input ...single label and multi class text classification paradigms ... See full document
6
Sentiment Classification in Resource-Scarce Languages by using Label Propagation
... graph-based propagation approach called Potts model (Wu, 1982) to solve a sentence-level sentiment classification ...to label propagation we used in this study, Potts model uses the ... See full document
10
Enhanced Weight Based Convolutional Neural Network (EWCNN) and Fuzzy Clustering For Semantically Rich Multi Label Social Emotion Classification
... emotion classification was enhanced through sentiment classification and this was the main target of this proposed ...emotion classification task very ...include semantic domain knowledge into ... See full document
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Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
... for relation classification by combining lexical and semantic ...learn semantic representations of the augmented dependency paths that are the combination of the shortest dependency paths and ... See full document
10
Supervised PU Learning for Cyber Security Event Prioritization
... In contrast to the AUC that evaluates the model on the whole test data, detection rate and lift reflect how good the model is in discovering risky hosts among different portions of predictions. To calculate these two ... See full document
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Crowd-sourced data collection to support automatic classification of building footprint data
... 2012). Crowdsourcing appeared first in Howe (2006) describing the business practice of outsourcing activity to the crowd, which is today an attractive way of acquiring cheap and fast annotations from non-expert ... See full document
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Self Crowdsourcing Training for Relation Extraction
... In this paper, we have proposed a self-training strategy for crowdsourcing as an effective alterna- tive to train annotators with Gold Standard. Our experimental results show that the annotations of workers ... See full document
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Simple Customization of Recursive Neural Networks for Semantic Relation Classification
... tion 2.2 for this particular task. There are two fac- tors: syntactic heads and syntactic path between tar- get entities. Our model puts a weight β ∈ [0.5, 1] on head phrases, and 1 − β on the others. For re- lation ... See full document
5
Weakly Supervised Cross lingual Semantic Relation Classification via Knowledge Distillation
... include semantic relations other than syn- onymy in practice, as can be seen (Table 1) in ex- amples drawn from the MUSE dictionary (Lam- ple et ...of semantic relations discov- ered in word-aligned ... See full document
12
Crowdsourcing Annotation of Non Local Semantic Roles
... years, crowdsourcing, ...with crowdsourcing; nevertheless it is also a crucial prerequisite for high-performance frame- semantic role labeling (SRL) systems (Das et ...the semantic roles ... See full document
5
Structured Lexical Similarity via Convolution Kernels on Dependency Trees
... In this paper, we propose a study of convolution kernels for dependency structures aiming at jointly modeling syntactic and lexical semantic similarity. More precisely, we define several dependency trees ... See full document
13
Crowdsourcing as a preprocessing for complex semantic annotation tasks
... We propose that a two-step annotation setup, where (i) the dataset annotation is crowdsourced and (ii) the low- agreement examples are re-annotated by experts. This strat- egy can alleviate the time bottleneck caused by ... See full document
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