English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data representation is a fundamental task in machine learning. Therepresentation of data affects the performance of the whole machine learningsystem. In a long history, the representation of data is done by featureengineering, and researchers aim at designing better features for specifictasks. Recently, the rapid development of deep learning and representationlearning has brought new inspiration to various domains.  In natural language processing, the most widely used feature representationis the Bag-of-Words model. This model has the data sparsity problem and cannotkeep the word order information. Other features such as part-of-speech taggingor more complex syntax features can only fit for specific tasks in most cases.This thesis focuses on word representation and document representation. Wecompare the existing systems and present our new model.  First, for generating word embeddings, we make comprehensive comparisonsamong existing word embedding models. In terms of theory, we figure out therelationship between the two most important models, i.e., Skip-gram and GloVe.In our experiments, we analyze three key points in generating word embeddings,including the model construction, the training corpus and parameter design. Weevaluate word embeddings with three types of tasks, and we argue that theycover the existing use of word embeddings. Through theory and practicalexperiments, we present some guidelines for how to generate a good wordembedding.  Second, in Chinese character or word representation. We introduce the jointtraining of Chinese character and word. ...  Third, for document representation, we analyze the existing documentrepresentation models, including recursive NNs, recurrent NNs and convolutionalNNs. We point out the drawbacks of these models and present our new model, therecurrent convolutional neural networks. ...

Word and Document Embeddings based on Neural Network Approaches