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

This paper presents a method for inducing a context-sensitive conditional probability context-free grammar from an unlabeled bracketed corpus using local contextual information and describes a natural language parsing model which uses a probabilitybased scoring function of the grammar to rank parses of a sentence. This method uses clustering techniques to group brackets in a corpus into a number of similar bracket groups based on their local contextual information. From the set of these groups, the corpus is automatically labeled with some nonterminal labels, and consequently a grammar with conditional probabilities is acquired. Based on these conditional probabilities, the statistical parsing model provides a framework for finding the most likely parse of a sentence. A number of experiments are made using EDR corpus and Wall Street Journal corpus. The results show that our approach achieves a relatively high accuracy: 88% recall, 72% precision and 0.7 crossing brackets per sentence for sentences shorter than 10 words, and 71% recall, 51% precision and 3.4 crossing brackets for sentences between 10-19 words. This result supports the assumption that local contextual statistics obtained from an unlabeled bracketed corpus are effective for learning a useful grammar and parsing.

Grammar Acquisition and Statistical Parsing by Exploiting Local Contextual Information