A brief overview of universal sentence representation methods: A linguistic view
How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …
embedding form is a fundamental problem in natural language processing. An informative …
Evaluating embeddings from pre-trained language models and knowledge graphs for educational content recommendation
Educational content recommendation is a cornerstone of AI-enhanced learning. In particular,
to facilitate navigating the diverse learning resources available on learning platforms …
to facilitate navigating the diverse learning resources available on learning platforms …
Convolution–deconvolution word embedding: An end-to-end multi-prototype fusion embedding method for natural language processing
Existing unsupervised word embedding methods have been proved to be effective to
capture latent semantic information on various tasks of Natural Language Processing (NLP) …
capture latent semantic information on various tasks of Natural Language Processing (NLP) …
A novel model for imbalanced data classification
Recently, imbalanced data classification has received much attention due to its wide
applications. In the literature, existing researches have attempted to improve the …
applications. In the literature, existing researches have attempted to improve the …
A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA
Effectively detecting supportive knowledge of answers is a fundamental step towards
automated question answering. While pre-trained semantic vectors for texts have enabled …
automated question answering. While pre-trained semantic vectors for texts have enabled …
A knowledge-enriched ensemble method for word embedding and multi-sense embedding
Representing words as embeddings has been proven to be successful in improving the
performance in many natural language processing tasks. Different from the traditional …
performance in many natural language processing tasks. Different from the traditional …
Generalized ambiguity decomposition for ranking ensemble learning
Error decomposition analysis is a key problem for ensemble learning, which indicates that
proper combination of multiple models can achieve better performance than any individual …
proper combination of multiple models can achieve better performance than any individual …
A Hybrid Approach for Binary Classification of Imbalanced Data
Binary classification with an imbalanced dataset is challenging. Models tend to consider all
samples as belonging to the majority class. Although existing solutions such as sampling …
samples as belonging to the majority class. Although existing solutions such as sampling …
Effective Diversity Optimizations for High Accuracy Deep Ensembles
Deep Neural Network Ensembles (Deep Ensembles) have emerged as a popular technique
for enhancing overall prediction accuracy by leveraging the complementary predictive …
for enhancing overall prediction accuracy by leveraging the complementary predictive …
Multilingual music genre embeddings for effective cross-lingual music item annotation
Annotating music items with music genres is crucial for music recommendation and
information retrieval, yet challenging given that music genres are subjective concepts …
information retrieval, yet challenging given that music genres are subjective concepts …