intent recognition

Tencent Docs Tech Team

Nov 13, 2024 · Artificial Intelligence

Technical Architecture and Practices of the AI Document Assistant

This article explores the challenges large language models bring to efficiency tools, outlines the AI document assistant's technical thinking and architecture, and details both application‑side and model‑side practices such as retrieval‑augmented generation, intent recognition, and code‑driven table handling, concluding with key lessons.

AIAI architectureDocument Automation

0 likes · 16 min read

Technical Architecture and Practices of the AI Document Assistant

HelloTech

Sep 13, 2023 · Artificial Intelligence

AI Platform‑Powered Automated Ticket Routing: Modeling Workflow, Feature Engineering, and Intent Recognition

The Haro AI platform automates customer‑service ticket routing by applying a four‑step pipeline—feature processing, model training, evaluation, and deployment—using BERT/ALBERT‑based intent recognition, configurable feature storage, AutoML or expert modes, and Faas‑style deployment, as demonstrated in the Universal Ticket System case study, dramatically improving accuracy and efficiency.

AI PlatformALBERTBERT

0 likes · 11 min read

AI Platform‑Powered Automated Ticket Routing: Modeling Workflow, Feature Engineering, and Intent Recognition

HelloTech

Jun 21, 2023 · Artificial Intelligence

Overview of Haro Intelligent Customer Service: Algorithms, Challenges, and AI Solutions

Haro’s intelligent customer service combines a smart FAQ recommender and a conversational chatbot that leverages matching‑based intent recognition, large‑scale domain pre‑training, metric‑learning for new intents, and fine‑tuned generative LLMs, achieving 82 % top‑1 accuracy while reducing human workload and outlining future API‑orchestrated, multimodal AI enhancements.

AINLPcustomer service

0 likes · 10 min read

Overview of Haro Intelligent Customer Service: Algorithms, Challenges, and AI Solutions

DataFunSummit

Nov 20, 2022 · Artificial Intelligence

NLP Technology Applications and Research in Voice Assistants

This article presents an in‑depth overview of NLP techniques used in voice assistants, covering the end‑to‑end conversational AI pipeline, intent and slot modeling, multi‑turn dialog management, model deployment pipelines, quantization methods, and self‑learning strategies for continuous improvement.

Model QuantizationNLPVoice Assistant

0 likes · 30 min read

NLP Technology Applications and Research in Voice Assistants

Ctrip Technology

Nov 10, 2022 · Artificial Intelligence

Improving Search Intent Recognition and Term Weighting with Deep Learning and Model Distillation at Ctrip

This article describes how Ctrip's R&D team applied deep‑learning models, BERT‑based embeddings, knowledge distillation, and term‑weighting techniques to enhance e‑commerce search intent recognition and term importance estimation, achieving high accuracy while meeting sub‑10 ms latency requirements.

BERTModel DistillationSearch

0 likes · 12 min read

Improving Search Intent Recognition and Term Weighting with Deep Learning and Model Distillation at Ctrip

DataFunTalk

Mar 17, 2022 · Artificial Intelligence

A Survey of Text Classification and Intent Recognition: Industrial and Research Perspectives

This article reviews recent developments in text classification and intent recognition, comparing industrial practices such as business‑coupled feature engineering with research trends like pretrained language models, and provides references and practical insights for building effective NLP solutions.

NLPText Classificationindustry applications

0 likes · 13 min read

A Survey of Text Classification and Intent Recognition: Industrial and Research Perspectives

DataFunTalk

Dec 16, 2021 · Artificial Intelligence

OPPO XiaoBu Assistant: Building a Low‑Code, End‑to‑End Dialogue System Platform

This article presents OPPO's XiaoBu Assistant platform, detailing its low‑code workflow for business domain modeling, multi‑type NLU (model‑based, retrieval‑based, and QA), componentized core services, large‑scale text processing, vector retrieval, and flexible dialogue management that together enable a complete skill lifecycle from development to online optimization.

AINLUconversation management

0 likes · 18 min read

OPPO XiaoBu Assistant: Building a Low‑Code, End‑to‑End Dialogue System Platform

58 Tech

Nov 18, 2021 · Artificial Intelligence

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

This article describes how 58 recruitment revamped its search system by removing rigid category limits, introducing query rewriting, intent recognition, doc understanding, and vector‑based recall, resulting in significantly higher relevance, reduced bad cases, and improved commercial performance.

AIRecruitmentSearch

0 likes · 14 min read

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

Xianyu Technology

Aug 4, 2021 · Artificial Intelligence

Design and Impact of a Chat Assistant for E‑commerce Negotiation and Greeting

The study presents an asynchronous chat‑assistant for second‑hand e‑commerce that uses intent recognition, price‑strength data, and filtered messaging to generate friendly bargaining and greeting scripts, achieving a 4 % rise in reply and conversion rates, and outlines plans to add more intents and richer product extraction.

Chatbotconversation AIe-commerce

0 likes · 8 min read

Design and Impact of a Chat Assistant for E‑commerce Negotiation and Greeting

Zhengtong Technical Team

Jun 5, 2020 · Artificial Intelligence

Design and Implementation of an Intelligent Chatbot System: Intent Recognition Algorithms and Architecture

The article details the architecture and intent recognition mechanisms of an AI chatbot for urban management, exploring regex matching, Levenshtein distance, and Naive Bayes classification, alongside dynamic model training and frontend data rendering strategies.

AI System DesignChatbot ArchitectureLevenshtein Distance

0 likes · 11 min read

Design and Implementation of an Intelligent Chatbot System: Intent Recognition Algorithms and Architecture

HomeTech

Nov 20, 2019 · Artificial Intelligence

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.

NLPintent recognitionknowledge graph

0 likes · 16 min read

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

Ctrip Technology

Nov 7, 2019 · Artificial Intelligence

Intelligent Customer Service in Travel: System Architecture and Key Technologies

This article explains the architecture and core technologies of Ctrip’s intelligent travel customer service, covering NLU, dialogue state tracking, policy learning, intent and slot extraction, multi‑round task bots, and the supporting platform for deployment and future multimodal extensions.

AIChatbotDialogue Management

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Intelligent Customer Service in Travel: System Architecture and Key Technologies

58 Tech

Oct 16, 2019 · Artificial Intelligence

Design and Implementation of Intent Recognition, Semantic Similarity Matching, and Slot Filling for a Voice Robot

This article details the architecture and algorithms behind a voice robot's natural language understanding module, covering single‑sentence intent classification with TextCNN, acoustic quality detection using VGGish‑BiLSTM, semantic similarity matching via DSSM and TextCNN‑Transformer, and slot‑filling with IDCNN‑CRF, along with performance results and future directions.

AINLUTextCNN

0 likes · 11 min read

Design and Implementation of Intent Recognition, Semantic Similarity Matching, and Slot Filling for a Voice Robot

Qunar Tech Salon

Oct 10, 2019 · Artificial Intelligence

Intelligent Customer Service System for Airline Ticket Business: Architecture, Data Analysis, and AI Techniques

This article describes the design and implementation of an AI‑powered intelligent customer service system for airline ticket operations, covering data‑driven problem analysis, dialogue architecture, intent recognition using BERT and fastText, knowledge‑base QA, and future development plans.

AIBERTIntelligent Customer Service

0 likes · 11 min read

Intelligent Customer Service System for Airline Ticket Business: Architecture, Data Analysis, and AI Techniques

58 Tech

Jul 4, 2019 · Artificial Intelligence

TaskBot Task-Oriented Dialogue System: Intent‑Entity Joint Recognition and Dialogue Management

The article presents TaskBot, a modular task‑oriented dialogue robot that uses a Bi‑LSTM‑CRF joint intent‑entity model and a state‑machine based dialogue manager to handle multi‑turn conversations such as flight booking or rental housing, detailing its architecture, implementation, and performance.

AIDialogue ManagementNLP

0 likes · 12 min read

TaskBot Task-Oriented Dialogue System: Intent‑Entity Joint Recognition and Dialogue Management

58 Tech

May 28, 2019 · Artificial Intelligence

Architecture and Design of an AI‑Powered Voice Robot System

The article describes the design and implementation of a voice robot platform, covering its background, layered architecture, dialogue flow, intent recognition techniques, micro‑service backend, and future improvements, highlighting how AI models and telephony integration enable automated multi‑turn voice interactions for sales and service scenarios.

MicroservicesSpeech AIVoice Bot

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Architecture and Design of an AI‑Powered Voice Robot System

DataFunTalk

May 23, 2019 · Artificial Intelligence

AI Techniques in Xiaomi Mobile Search: Text Relevance, Intent Recognition, and Click‑Model Ranking

The article presents Xiaomi's mobile search system, detailing how AI methods such as deep learning, GBDT and DNN models are applied to text relevance calculation, intent detection with term‑weighting, and click‑through ranking models (PBM, Cascade, DBN) to improve user experience across heterogeneous result types.

AIRankingSearch

0 likes · 9 min read

AI Techniques in Xiaomi Mobile Search: Text Relevance, Intent Recognition, and Click‑Model Ranking

DataFunTalk

Mar 8, 2019 · Artificial Intelligence

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

The article presents an in‑depth overview of Alibaba's intelligent service bot Ali Xiaomì, covering its platform evolution, core NLP techniques such as intent recognition and machine reading comprehension, multi‑turn recommendation strategies, transfer‑learning approaches across domains and languages, and future technical challenges.

AIChatbotNatural Language Processing

0 likes · 11 min read

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

JD Tech

Jan 16, 2019 · Artificial Intelligence

Technical Deep Dive of JD’s Intelligent Customer Service 2.0: AI‑Driven Intent Recognition, Emotion Analysis, and Smart Scheduling

This article presents a comprehensive technical analysis of JD’s Intelligent Customer Service 2.0, detailing AI‑based intent recognition with the ABSQ framework, hierarchical attention networks, emotion analysis via CNN, speech navigation using ASR/NLP, and machine‑learning‑driven smart dispatch that together boost accuracy and user experience.

AISmart SchedulingSpeech Recognition

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Technical Deep Dive of JD’s Intelligent Customer Service 2.0: AI‑Driven Intent Recognition, Emotion Analysis, and Smart Scheduling

iQIYI Technical Product Team

Sep 14, 2018 · Artificial Intelligence

Limitations of Language Models in Voice Interaction and HomeAI Solutions

iQIYI HomeAI tackles the bottleneck of static language models in voice assistants by separating phonetic and semantic processing, correcting ASR errors at the intent‑recognition layer with pinyin‑enhanced entity correction, thereby reducing error amplification in video‑on‑demand interactions and paving the way for adaptive, personalized voice experiences.

AISpeech Recognitionintent recognition

0 likes · 7 min read

Limitations of Language Models in Voice Interaction and HomeAI Solutions