In the previous articles, I discussed query normalisation, query enrichment, and intent classification. These methods help a system clean up the user's language, generate alternative query forms, and determine what the user is broadly trying to do. However, identifying the intent is not enough. Consider the following question: Where does the floating rate index map … Continue reading AI-Native Financial Data Foundation (44): Mention Extraction, Context Interpretation, and Expected Entity Types
Tag: Semantic Query
AI-Native Financial Data Foundation (43): Intent Classification for Semantic Query
This blog article focuses on three questions: Why is intent classification needed? What should an intent represent? What classification approaches are available? The aim is not to provide a complete machine-learning survey. It is to explain the main design choices and how they support semantic query. 1. Why Intent Classification Is Needed Intent classification identifies … Continue reading AI-Native Financial Data Foundation (43): Intent Classification for Semantic Query
AI-Native Financial Data Foundation (42): An Exhaustive List of Query Normalisation Methods
This is intentionally a technical, list-style article. Its purpose is to provide a reasonably exhaustive catalogue of candidate techniques for reducing superficial language variation without changing the user's intended business meaning. Rather than proposing one preferred normalisation approach, it is intended to serve as a reference for designing the query-planning service: which techniques are available, … Continue reading AI-Native Financial Data Foundation (42): An Exhaustive List of Query Normalisation Methods
Data Ninjago (Financial Data Architecture) 
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