A Rule-Based Capitalization Algorithm Using NLP for Text Formatting Consistency

Abstract

Capitalization is essential in making texts readable, well-structured, and meaningful. This paper discusses the creation of a rule-based capitalization algorithm based on Natural Language Processing (NLP) methods for improving text formatting consistency. A representative dataset including news headlines, academic articles, and book titles is collected to achieve generalizability across text domains. The preprocessing step entails tokenization and part-of-speech (POS) tagging for categorizing words into notional (e.g., nouns, verbs, adjectives) and non-notional categories (e.g., articles, conjunctions, and brief prepositions). This categorization forms the basis for the structured capitalization rule application. The algorithm suggested has a systematic pipeline of tokenization, POS tagging, application of capitalization rules, and reconstruction of text. Executed with Python and NLP libraries like NLTK and spaCy, the algorithm capitalizes all notional words following title case conventions while maintaining linguistic and structural precision. The effectiveness of the algorithm is measured against manually formatted title case text and compared with available online converters for benchmarking. Precision, recall, and F1-score are used as performance metrics to measure accuracy and efficiency, and high reliability was shown in capitalizing text with few errors. A confusion matrix is utilized to examine classification accuracy, grouping outputs into true positives, false positives, false negatives, and true negatives. A Random Forest Model is also utilized to measure feature importance, with text reconstruction and exception handling emerging as central drivers of capitalization accuracy. The findings demonstrate that optimizing these elements greatly improves algorithm performance. The contribution of this work is to NLP-based text processing in presenting a rule-based, structured approach to capitalization that has implications in automated publishing, text formatting, and standardizing content.