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

We present a framework for the induction of semantic frames from utterancesin the context of an adaptive command-and-control interface. The system istrained on an individual user's utterances and the corresponding semanticframes representing controls. During training, no prior information on thealignment between utterance segments and frame slots and values is available.In addition, semantic frames in the training data can contain information thatis not expressed in the utterances. To tackle this weakly supervisedclassification task, we propose a framework based on Hidden Markov Models(HMMs). Structural modifications, resulting in a hierarchical HMM, and anextension called expression sharing are introduced to minimize the amount oftraining time and effort required for the user.  The dataset used for the present study is PATCOR, which contains commandsuttered in the context of a vocally guided card game, Patience. Experimentswere carried out on orthographic and phonetic transcriptions of commands,segmented on different levels of n-gram granularity. The experimental resultsshow positive effects of all the studied system extensions, with some effectdifferences between the different input representations. Moreover, evaluationexperiments on held-out data with the optimal system configuration show thatthe extended system is able to achieve high accuracies with relatively smallamounts of training data.

Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models