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

Extracting aspect-polarity pairs from texts is an important task offine-grained sentiment analysis. While the existing approaches to this taskhave gained many progresses, they are limited at capturing relationships amongaspect-polarity pairs in a text, thus degrading the extraction performance.Moreover, the existing state-of-the-art approaches, namely token-basedse-quence tagging and span-based classification, have their own defects such aspolarity inconsistency resulted from separately tagging tokens in the formerand the heterogeneous categorization in the latter where aspect-related andpolarity-related labels are mixed. In order to remedy the above defects,in-spiring from the recent advancements in relation extraction, we propose togenerate aspect-polarity pairs directly from a text with relation extractiontechnology, regarding aspect-pairs as unary relations where aspects areenti-ties and the corresponding polarities are relations. Based on theperspective, we present a position- and aspect-aware sequence2sequence modelfor joint extraction of aspect-polarity pairs. The model is characterized withits ability to capture not only relationships among aspect-polarity pairs in atext through the sequence decoding, but also correlations between an aspect andits polarity through the position- and aspect-aware attentions. Theexperi-ments performed on three benchmark datasets demonstrate that our modeloutperforms the existing state-of-the-art approaches, making significantim-provement over them.

Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach