Five Intent Recognition Designs: From Keyword Matching to Classifier to LLM Self‑Routing – A Decision Tree to Choose the Right One

The article breaks down five production‑grade intent‑recognition designs—keyword matching, regex‑rule engine, embedding classifier, fine‑tuned small model, and zero‑shot LLM routing—provides code snippets, latency and cost benchmarks, decision‑making rules, and shows how a layered architecture can cut API costs from ¥80,000 to ¥3,000 while keeping accuracy above 90%.

LLM routingembedding classifierfine‑tune model

0 likes · 16 min read

Five Intent Recognition Designs: From Keyword Matching to Classifier to LLM Self‑Routing – A Decision Tree to Choose the Right One

Sohu Tech Products

Nov 22, 2023 · Backend Development

Optimizing a Real‑Time Keyword Matching Service with Aho‑Corasick and Double‑Array Trie

By replacing the naïve double‑loop matcher with a Double‑Array Trie‑based Aho‑Corasick automaton and refactoring the system into a layered name‑and‑data microservice architecture that shards the keyword dictionary and rebuilds the automaton only on version changes, the real‑time keyword‑matching service reduced latency from seconds to milliseconds even at thousands of QPS.

Aho-CorasickMicroservicesTrie

0 likes · 17 min read

Optimizing a Real‑Time Keyword Matching Service with Aho‑Corasick and Double‑Array Trie

Python Crawling & Data Mining

Aug 22, 2023 · Fundamentals

Label Excel Rows by Keywords with Pandas: A Quick Python Guide

This article demonstrates how to use Python's pandas library to read an Excel file, define a function that matches specific brand keywords in a column, assign corresponding labels such as “automobile brand” or “sports brand” to a new column, and output the results, while also providing the full code example and tips for sharing data and seeking help.

ExcelPythondata-processing

0 likes · 5 min read

Label Excel Rows by Keywords with Pandas: A Quick Python Guide

58 Tech

Dec 25, 2020 · Artificial Intelligence

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

This article presents a comprehensive study on C‑side user identity recognition for internet platforms, addressing cold‑start and sample‑scarcity challenges by comparing keyword matching, XGBoost, TextCNN, a fusion model, and an improved Wide & Deep architecture, showing that the latter achieves the highest F1 score of 80.67%.

Model EvaluationTextCNNWide&Deep

0 likes · 13 min read

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

DataFunTalk

Sep 21, 2020 · Artificial Intelligence

Data‑Driven Synonym Transformation for Keyword Matching in Search Advertising

This article explains how keyword matching in search advertising works, outlines the challenges of semantic gaps, matching‑mode determination and scalability, and describes data‑driven synonym transformation techniques—including rule‑based, sequence‑to‑sequence, metric‑space and graph‑based models—to improve recall, efficiency, and robustness.

Ad Techkeyword matchingmachine learning

0 likes · 18 min read