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

Despite the number of NLP studies dedicated to thematic fit estimation,little attention has been paid to the related task of composing and updatingverb argument expectations. The few exceptions have mostly modeled thisphenomenon with structured distributional models, implicitly assuming asimilarly structured representation of events. Recent experimental evidence,however, suggests that human processing system could also exploit anunstructured "bag-of-arguments" type of event representation to predictupcoming input. In this paper, we re-implement a traditional structured modeland adapt it to compare the different hypotheses concerning the degree ofstructure in our event knowledge, evaluating their relative performance in thetask of the argument expectations update.

Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?