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041 _aengtag
050 _aQA76.9 A43 R63 2025
082 _a.
100 1 _a Robantes, Jhan Lou P.; Serrano, Andreo A.
245 _aEnhanced named entity recognition algorithm for Filipino cultural and heritage texts
264 1 _a.
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300 _bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336 _2text
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337 _2unmediated
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338 _2volume
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505 _aABSTRACT: Named Entity Recognition (NER) is a crucial natural language processing task that extracts and classifies named entities from unstructured text into predefined categories. While existing NER methods have shown success in general domains, they often face significant challenges when applied to specialized context like Filipino cultural and historical texts. These challenges stem from unique linguistics features, and diverse naming conventions. This research introduces an enhanced rule-based NER approach that specifically addresses these challenges. At its core, the system utilizes curated Corpus of Historical Filipino and Philippine English (COHFIE), which serves as both training and evaluation data. This research an enhanced rule-based approach for NER using a Corpus of Historical Filipino and Philippine English (COHFIE) building on pattern-learning methods, incorporating character and token features, and by using positive and negative example sets. To enrich the classification process, we used the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM), a cultural heritage framework, to provide a more nuanced categorization of entities based on their historical and cultural significance. Tested across existing Filipino based models (calamanCy and RoBERTa), the enhanced model shows improvement on identifying entities related to Filipino culture (CUL) and history terms (PER, ORG, LOC).
526 _aF
655 _aacademic writing
942 _2lcc
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999 _c37355
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