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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Probabilistic context-free grammars (PCFGs) are a widely known class of probabilistic language models. The Inside-Outside (I-O) algorithm is well known as an efficient EM algorithm tailored for PCFGs. Although the algorithm requires inexpensive linguistic resources, there remains a problem in its efficiency. This paper presents an efficient method for training PCFG parameters in which the parser is separated from the EM algorithm, assuming that the underlying CFG is given. A new EM algorithm exploits the compactness of well-formed substring tables (WFSTs) generated by the parser. Our proposal is general in that the input grammar need not take Chomsky normal form (CNF) while it is equivalent to the I-O algorithm in the CNF case. In addition, we propose a polynomial-time EM algorithm for CFGs with contextsensitive probabilities, and report experimental results with the ATR dialogue corpus and a hand-crafted Japanese grammar.

Using WFSTs for Efficient EM Learning of Probabilistic CFGs and Their Extensions