Abstract Ambiguity detection in context-free grammars (CFGs) is critical for parsing programming languages, yet it is undecidable in the general case. Traditional methods, such as exhaustive search and approximation techniques, either struggle with scalability or risk false positives. This paper introduces a novel search-based approach, embodied in the prototype tool SinBAD, for detecting ambiguity in context-free grammars (CFGs). SinBAD employs random search techniques to generate... Ambiguity detection in context-free grammars (CFGs) is critical for parsing programming languages, yet it is undecidable in the general case. Traditional methods, such as exhaustive search and approximation techniques, either struggle with scalability or risk false positives. This paper introduces a novel search-based approach, embodied in the prototype tool SinBAD, for detecting ambiguity in context-free grammars (CFGs). SinBAD employs random search techniques to generate sentences from a given grammar and uses an Earley-based parser to identify ambiguous parses. The tool's architecture supports configurable backends that influence sentence generation strategies. An extensive experiment compares SinBAD against existing tool-ACLA (approximation-based) and AmbiDexter (hybrid)-on two datasets: randomly generated CFGs and manually altered ambiguous grammars from real programming languages (Pascal, SQL, Java, C). Results show that SinBAD detects more ambiguities in random grammars within shorter time frames and performs comparably on programming language grammars. The study highlights the strengths of random search in exploring diverse parts of the search space, though results vary across runs. The paper concludes with a discussion on the limitations of the random grammar generator. It suggests future directions, including expanding experiments to larger, real-world grammars and exploring additional search-based techniques. Read More Read Less

Abstract To extract key value pairs from documents like resumes, we have to use various processing techniques to get relevant information. The extracted information encompasses a diverse range of factors, including education, experience, skills, interests, and employment history. By gathering such information and presenting it in a structured format, hiring managers can thoroughly understand and evaluate the backgrounds of applying candidates, thereby simplifying the entire... To extract key value pairs from documents like resumes, we have to use various processing techniques to get relevant information. The extracted information encompasses a diverse range of factors, including education, experience, skills, interests, and employment history. By gathering such information and presenting it in a structured format, hiring managers can thoroughly understand and evaluate the backgrounds of applying candidates, thereby simplifying the entire recruitment process. This versatility allows organisations to leverage various techniques and technologies to extract valuable information from resumes, enabling them to streamline their hiring processes and make informed decisions about their workforce. Read More Read Less

Abstract This paper is not just an analytical tool, but as a potential blueprint for generating Russian folk tales. It argues that while Propp's formalism has inspired many story generation systems, its specific procedural aspects for generation have been largely overlooked or misapplied, often leading to limitations. The authors propose a computational implementation focusing on Propp's core concepts: character functions, their sequence, and dependencies. They... This paper is not just an analytical tool, but as a potential blueprint for generating Russian folk tales. It argues that while Propp's formalism has inspired many story generation systems, its specific procedural aspects for generation have been largely overlooked or misapplied, often leading to limitations. The authors propose a computational implementation focusing on Propp's core concepts: character functions, their sequence, and dependencies. They develop a system using plot drivers (function sequences), fabula generators (instantiating functions with actions linked by preconditions/postconditions), and flows (final story representation). Evaluation focuses on structural adherence and continuity, demonstrating that methods employing unification and accommodation yield more cohesive narratives. The paper concludes that a declarative, modular approach based closely on Propp's original procedural description is viable for generating structurally sound tales within the specific domain of Russian folklore, offering potential for adaptation to other narrative structures. Read More Read Less