Contextual Embeddings for Word Sense Disambiguation in Natural Language Queries to Knowledge Bases

Abstract

Contextual embeddings have emerged as a powerful tool for resolving ambiguities that arise when individuals or automated agents query knowledge bases using natural language. By capturing dynamic linguistic information based on surrounding text, such embeddings allow for more nuanced interpretations of words with multiple possible meanings. One core challenge is to map ambiguous tokens onto the correct semantic representation within a structured knowledge base without introducing extraneous or erroneous associations. This is achieved by analyzing each token in the broader syntactic and conceptual frame in which it appears. To achieve accurate alignments, one can employ context-sensitive representation models that integrate local dependencies and global relational cues. The approach involves implementing sophisticated similarity metrics that account for shared substructures and logical constraints. Such a methodology avoids rigid, static encoding schemes and instead adapts to variations in phrasing, domain-specific jargon, and specialized terminologies. Models may be refined through iterative optimization steps aimed at reducing uncertainty in the disambiguation process, allowing for a gradual improvement in linking precision. These contextual embeddings ultimately facilitate robust natural language queries to knowledge bases by grounding words in the intended semantic context, leading to more reliable retrieval of pertinent information, reduced response latency, and the potential for broader applicability in diverse query environments.