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

Recurrent Neural Network (RNN) and one of its specific architectures, LongShort-Term Memory (LSTM), have been widely used for sequence labeling. In thispaper, we first enhance LSTM-based sequence labeling to explicitly model labeldependencies. Then we propose another enhancement to incorporate the globalinformation spanning over the whole input sequence. The latter proposed method,encoder-labeler LSTM, first encodes the whole input sequence into a fixedlength vector with the encoder LSTM, and then uses this encoded vector as theinitial state of another LSTM for sequence labeling. Combining these methods,we can predict the label sequence with considering label dependencies andinformation of whole input sequence. In the experiments of a slot filling task,which is an essential component of natural language understanding, with usingthe standard ATIS corpus, we achieved the state-of-the-art F1-score of 95.66%.

Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling