Large-scale hierarchical text classification with recursively regularized deep graph-cnn
Text classification to a hierarchical taxonomy of topics is a common and practical problem.
Traditional approaches simply use bag-of-words and have achieved good results. However …
Traditional approaches simply use bag-of-words and have achieved good results. However …
A survey of text classification algorithms
CC Aggarwal, CX Zhai - Mining text data, 2012 - Springer
The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …
database, and information retrieval communities with applications in a number of diverse …
A survey of hierarchical classification across different application domains
CN Silla, AA Freitas - Data mining and knowledge discovery, 2011 - Springer
In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …
is scattered across very different application domains and for that reason research in one …
Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation
learning and are widely used in various text mining tasks such as large-scale multi-label text …
learning and are widely used in various text mining tasks such as large-scale multi-label text …
Multi-label text classification using attention-based graph neural network
In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It
is observed that most MLTC tasks, there are dependencies or correlations among labels …
is observed that most MLTC tasks, there are dependencies or correlations among labels …
Modeling dwell time to predict click-level satisfaction
Clicks on search results are the most widely used behavioral signals for predicting search
satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy …
satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy …
Clustering and diversifying web search results with graph-based word sense induction
A Di Marco, R Navigli - Computational Linguistics, 2013 - direct.mit.edu
Web search result clustering aims to facilitate information search on the Web. Rather than
the results of a query being presented as a flat list, they are grouped on the basis of their …
the results of a query being presented as a flat list, they are grouped on the basis of their …
Recursive regularization for large-scale classification with hierarchical and graphical dependencies
The two key challenges in hierarchical classification are to leverage the hierarchical
dependencies between the class-labels for improving performance, and, at the same time …
dependencies between the class-labels for improving performance, and, at the same time …
Refined experts: improving classification in large taxonomies
PN Bennett, N Nguyen - Proceedings of the 32nd international ACM …, 2009 - dl.acm.org
While large-scale taxonomies--especially for web pages--have been in existence for some
time, approaches to automatically classify documents into these taxonomies have met with …
time, approaches to automatically classify documents into these taxonomies have met with …
[PDF][PDF] Inducing word senses to improve web search result clustering
R Navigli, G Crisafulli - Proceedings of the 2010 conference on …, 2010 - aclanthology.org
In this paper, we present a novel approach to Web search result clustering based on the
automatic discovery of word senses from raw text, a task referred to as Word Sense …
automatic discovery of word senses from raw text, a task referred to as Word Sense …