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 ambiguities introduced by the recombination of morphemes constructingseveral possible inflections for a word makes the prediction of syntactictraits in Morphologically Rich Languages (MRLs) a notoriously complicated task.We propose the Multi Task Deep Morphological analyzer (MT-DMA), acharacter-level neural morphological analyzer based on multitask learning ofword-level tag markers for Hindi and Urdu. MT-DMA predicts a set of sixmorphological tags for words of Indo-Aryan languages: Parts-of-speech (POS),Gender (G), Number (N), Person (P), Case (C), Tense-Aspect-Modality (TAM)marker as well as the Lemma (L) by jointly learning all these in one trainableframework. We show the effectiveness of training of such deep neural networksby the simultaneous optimization of multiple loss functions and sharing ofinitial parameters for context-aware morphological analysis. Exploitingcharacter-level features in phonological space optimized for each tag usingmulti-objective genetic algorithm, our model establishes a new state-of-the-artaccuracy score upon all seven of the tasks for both the languages. MT-DMA ispublicly accessible: code, models and data are available athttps://github.com/Saurav0074/morph_analyzer.

Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction