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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recurrent Neural Networks (RNNs) are widely used in the field of naturallanguage processing (NLP), ranging from text categorization to questionanswering and machine translation. However, RNNs generally read the whole textfrom beginning to end or vice versa sometimes, which makes it inefficient toprocess long texts. When reading a long document for a categorization task,such as topic categorization, large quantities of words are irrelevant and canbe skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model whichdynamically leaps between words while reading texts. At each step, we utilizeseveral feature encoders to extract messages from preceding texts, followingtexts and the current word, and then determine whether to skip the currentword. We evaluate Leap-LSTM on several text categorization tasks: sentimentanalysis, news categorization, ontology classification and topicclassification, with five benchmark data sets. The experimental results showthat our model reads faster and predicts better than standard LSTM. Compared toprevious models which can also skip words, our model achieves better trade-offsbetween performance and efficiency.

Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization