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

The goal of our industrial ticketing system is to retrieve a relevantsolution for an input query, by matching with historical tickets stored inknowledge base. A query is comprised of subject and description, while ahistorical ticket consists of subject, description and solution. To retrieve arelevant solution, we use textual similarity paradigm to learn similarity inthe query and historical tickets. The task is challenging due to significantterm mismatch in the query and ticket pairs of asymmetric lengths, wheresubject is a short text but description and solution are multi-sentence texts.We present a novel Replicated Siamese LSTM model to learn similarity inasymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrievaltask, respectively over unsupervised and supervised baselines. We also showthat the topic and distributed semantic features for short and long textsimproved both similarity learning and retrieval.

Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts