Recognizing Bangla Grammar using Predictive Parser

We describe a Context Free Grammar (CFG) for Bangla language and hence we propose a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The proposed parser is a predictive parser and we construct the parse table for recognizing Bangla grammar. Using the parse table we recognize syntactical mistakes of Bangla sentences when there is no entry for a terminal in the parse table. If a natural language can be successfully parsed then grammar checking from this language becomes possible. The proposed scheme is based on Top down parsing method and we have avoided the left recursion of the CFG using the idea of left factoring.

💡 Research Summary

The paper “Recognizing Bangla Grammar using Predictive Parser” presents a method for syntactic analysis of Bangla (Bengali) sentences by constructing a context‑free grammar (CFG) tailored to the language and implementing a top‑down predictive parser based on that grammar. The authors begin by identifying the basic word order of Bangla (subject‑object‑verb) and the role of post‑positions, inflectional endings, and compound constructions. They then define a set of non‑terminal symbols (such as NP, VP, PP) and production rules that capture typical Bangla phrase structures. Because a naïve CFG for Bangla would contain left‑recursive rules, which are incompatible with LL(1) parsing, the authors systematically eliminate left recursion and apply left‑factoring to ensure that each non‑terminal has a unique look‑ahead choice. This transformation yields a grammar that satisfies the LL(1) condition, allowing the construction of a deterministic parsing table.

The predictive parser operates by consulting the parsing table: for a given non‑terminal on the stack and the next input token, the table entry provides the single production to apply. If no entry exists, the parser reports a syntactic error at that position. The parser therefore serves both as a recognizer of well‑formed Bangla sentences and as a simple grammar‑checking tool. The authors describe the manual computation of FIRST and FOLLOW sets, the filling of the parsing table, and the error‑detection mechanism that hinges on the absence of a table entry.

To evaluate the approach, the authors test the parser on a small set of manually crafted Bangla sentences, including both grammatically correct examples and intentionally malformed ones. The results show that all correct sentences are successfully parsed, and each malformed sentence triggers an error report precisely where the parsing table lacks a matching entry. However, the evaluation is limited in scope: the test corpus is tiny, does not cover the full range of Bangla morphological complexity, and lacks statistical performance metrics such as parsing time, memory consumption, or precision/recall on a larger benchmark.

The main contributions of the paper are: (1) a concrete CFG for Bangla that has been transformed into an LL(1) grammar; (2) the design and implementation of a predictive parser that uses a deterministic parsing table for Bangla; and (3) a demonstration that the parser can detect syntactic violations by simple table‑lookup failures. These contributions illustrate how classic compiler‑theory techniques can be adapted for natural‑language processing of a less‑studied language.

Nevertheless, several limitations are evident. Bangla exhibits rich morphology, extensive use of inflectional suffixes, and occasional word‑order flexibility that are difficult to capture fully with a pure CFG. The current system assumes that tokenization and morphological analysis have already been performed, which sidesteps a major challenge in Bangla NLP. The LL(1) predictive approach, while simple, cannot handle ambiguous or highly nested constructions without backtracking, and it provides no error‑recovery strategy; parsing halts as soon as an unexpected token is encountered. Moreover, the parser does not address context‑sensitive constraints such as subject‑verb agreement or semantic plausibility, limiting its usefulness as a comprehensive grammar checker.

Future work suggested by the authors includes integrating a morphological analyzer to automate token generation, exploring more powerful parsing algorithms (LR, GLR, Earley) that can accommodate ambiguous grammars, and implementing error‑recovery mechanisms to continue parsing after an error is detected. Expanding the evaluation to a large, diverse Bangla corpus and providing quantitative benchmarks for speed and memory usage would also be essential steps toward a practical, production‑grade Bangla grammar checking tool. In summary, the paper offers a valuable proof‑of‑concept that bridges formal language theory and Bangla syntax, but further development is required to address the full linguistic complexity and practical performance demands of real‑world Bangla text processing.