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

This paper presents an approach for building a corpus for the domain of motion and spatial inference using a specific class of verbs. The approach creates a distribution of inference features that maximize the discriminatory power of a system trained on the corpus. The paper addresses the issue of using an existing textual inference system for generating the examples. This enables the corpus annotation method to assert whether more data is necessary.

Building an annotated textual inference corpus for motion and space