Stochastic Approach of Parsing Bengali Sentences

The parsing technique based on associate grammar rules as well as probability is called stochastic parsing. This paper suggested a probabilistic method to eliminate the uncertainty from the sentences of Bangla. The technique of Binarization is applied to increase the precision of the parsing. CYK algorithm is used in this paper. The work mainly focused on intonation-based sentences, for these reasons PCFGs (Probabilistic Context-Free Grammars) is based on proposed. About 30324 words are used to test the proposed system; average 93% accuracy is achieved. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 51-56

From UBGs to CFGs A practical corpus-driven approach

We present a simple and intuitive unsound corpus-driven approximation method for turning unification-based grammars, such as HPSG, CLE, or PATR-II into context-free grammars (CFGs). Our research is motivated by the idea that we can exploit (large-scale), hand-written unification grammars not only for the purpose of describing natural language and obtaining a syntactic structure (and perhaps a semantic form), but also to address several other very practical topics. Firstly, to speed up deep parsing by having a cheap recognition pre-flter (the approximated CFG). Secondly, to obtain an indirect stochastic parsing model for the unification grammar through a trained PCFG, obtained from the approximated CFG. This gives us an efficient disambiguation model for the unification-based grammar. Thirdly, to generate domain-specific subgrammars for application areas such as information extraction or question answering. And finally, to compile context-free language models which assist the acoustic model of a speech recognizer. The approximation method is unsound in that it does not generate a CFG whose language is a true superset of the language accepted by the original unification-based grammar. It is a corpus-driven method in that it relies on a corpus of parsed sentences and generates broader CFGs when given more input samples. Our open approach can be fine-tuned in different directions, allowing us to monotonically come close to the original parse trees by shifting more information into the context-free symbols. The approach has been fully implemented in JAVA.