Natural Language Processing Pipelines for Automated Knowledge Base Population: Applying Named Entity Recognition and Dependency Parsing

Abstract

Natural language processing pipelines have become critical for automating knowledge base population, particularly through the integration of named entity recognition (NER) and dependency parsing. This paper presents a systematic framework for extracting structured knowledge from unstructured text by leveraging advances in sequence labeling, graph-based syntactic analysis, and probabilistic relational modeling. The proposed architecture combines bidirectional long short-term memory networks with conditional random fields to disambiguate entity boundaries and classify entities into predefined types under sparse and noisy textual conditions. Concurrently, a transition-based dependency parser augmented with attention mechanisms isolates grammatical relationships between entities, enabling the derivation of context-aware relational triples. A key innovation lies in the formulation of a joint optimization objective that aligns entity-relation pairs through tensor factorization, ensuring consistency between localized entity mentions and global knowledge graph semantics. Experiments demonstrate robustness to cross-domain syntactic variations and entity density fluctuations, achieving an F1 score of 92.3\% on entity typing and 88.7\% on relation extraction across multilingual benchmarks. The pipeline's computational complexity is analyzed through asymptotic bounds on graph traversal operations and entropy-regulated sampling strategies. This work establishes theoretical foundations for handling nested entity structures and discontinuous phrasal relations while maintaining linear time complexity relative to input sequence length, addressing critical scalability requirements for real-world knowledge base population systems.