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

This paper describes continuing work on semantic frame slot filling for acommand and control task using a weakly-supervised approach. We investigate theadvantages of using retraining techniques that take the output of ahierarchical hidden markov model as input to two inductive approaches: (1)discriminative sequence labelers based on conditional random fields andmemory-based learning and (2) probabilistic context-free grammar induction.Experimental results show that this setup can significantly improve F-scoreswithout the need for additional information sources. Furthermore, qualitativeanalysis shows that the weakly supervised technique is able to automaticallyinduce an easily interpretable and syntactically appropriate grammar for thedomain and task at hand.

A weakly supervised sequence tagging and grammar induction approach to semantic frame slot filling