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Sohu Tech Products
Sohu Tech Products
Nov 26, 2025 · Artificial Intelligence

How Cleanlab Cut Data Review by 34×: A Real‑World Text Classification Case Study

This article walks through a real text‑classification project where noisy labels inflated the review workload to over 15,000 samples, and shows how using cleanlab’s confident‑learning framework reduced the manual audit set to 438 items, boosting efficiency by thirty‑four times while improving model performance.

Data QualityData‑Centric AIcleanlab
0 likes · 16 min read
How Cleanlab Cut Data Review by 34×: A Real‑World Text Classification Case Study
Open Source Tech Hub
Open Source Tech Hub
Aug 22, 2025 · Artificial Intelligence

Automate User Feedback Classification with a Large‑Model API in PHP

This guide shows how to use the Tongyi Qianwen large‑model API with PHP to automatically classify user feedback into predefined categories, eliminating manual analysis and complex NLP development while providing clear steps, code, and result interpretation for rapid business insights.

APIAutomationPHP
0 likes · 7 min read
Automate User Feedback Classification with a Large‑Model API in PHP
OPPO Amber Lab
OPPO Amber Lab
Apr 26, 2024 · Artificial Intelligence

Deploy Efficient Text Classification on Android with TensorFlow Lite

This guide walks you through the end‑to‑end process of building, training, converting, and deploying a TensorFlow Lite text‑classification model on Android, covering data preparation, model selection, performance trade‑offs, and integration using the TFLite Task Library.

AndroidTensorFlow Litetext classification
0 likes · 19 min read
Deploy Efficient Text Classification on Android with TensorFlow Lite
Bilibili Tech
Bilibili Tech
Feb 18, 2024 · Artificial Intelligence

Bilibili Personal Attack Content Governance: Background, Goals, Methods, and Effectiveness

Bilibili combats personal‑attack and trolling comments by combining sector‑specific keyword databases, user‑group analysis, advanced word‑matching (including pinyin and homophone detection) and multiple NLP/graph models, which has cut personal‑attack reports in entertainment, film and gaming by about 32 % and trolling reports by roughly 25 % between June and December 2023.

Bilibiliabusive language detectioncontent moderation
0 likes · 12 min read
Bilibili Personal Attack Content Governance: Background, Goals, Methods, and Effectiveness
Sohu Tech Products
Sohu Tech Products
Sep 6, 2023 · Mobile Development

Building an iOS SMS Spam Filter App with CoreML

This tutorial walks through creating a custom iOS SMS spam filter app, covering extraction of personal SMS data from an iPhone backup, training a CoreML text‑classification model with CreateML, implementing a Message Filter Extension in Xcode, and exploring advanced update strategies.

App ExtensionCoreMLSMS filtering
0 likes · 12 min read
Building an iOS SMS Spam Filter App with CoreML
Sohu Tech Products
Sohu Tech Products
Jun 7, 2023 · Artificial Intelligence

Multiscale PU Learning for Detecting AI‑Generated Text

Researchers from Peking University and Huawei present a multiscale positive‑unlabeled learning framework that significantly improves detection of AI‑generated short and long texts, addressing the difficulty of distinguishing AI‑written content from human writing and outperforming existing baselines on multiple benchmarks.

AI detectionPu-Learninglarge language models
0 likes · 8 min read
Multiscale PU Learning for Detecting AI‑Generated Text
Bitu Technology
Bitu Technology
Jul 8, 2022 · Artificial Intelligence

Applying NLP and Machine Learning to Classify Tubi User Feedback

This article explains how Tubi leverages natural‑language processing, sentence embeddings (USE and BERT), and LightGBM models to automatically categorize large volumes of Net Promoter Score comments and customer‑support tickets, enabling data‑driven product decisions and workflow automation.

LightGBMNLPTubi
0 likes · 11 min read
Applying NLP and Machine Learning to Classify Tubi User Feedback
DataFunSummit
DataFunSummit
Jun 11, 2022 · Artificial Intelligence

Transforming Regular Expressions into Neural Networks for Text Classification and Slot Filling

This article explains how regular expressions can be converted into equivalent neural network models—FA‑RNN for classification and FST‑RNN for slot filling—by leveraging finite‑state automata, tensor decomposition, and pretrained word embeddings, achieving zero‑shot performance and strong results in low‑resource scenarios.

FA-RNNNeural Networksregular expressions
0 likes · 17 min read
Transforming Regular Expressions into Neural Networks for Text Classification and Slot Filling
Zuoyebang Tech Team
Zuoyebang Tech Team
Apr 15, 2022 · Artificial Intelligence

Zuoyebang’s NLP Platforms: Boosting Online Education with AI

In this interview, Zuoyebang’s NLP lead explains how the company built self‑developed platforms like IQC and FTP to automate text quality inspection and intelligent labeling, outlines their architecture, shares practical deep‑learning applications such as translation and grammar correction, and discusses future research directions in large‑scale multi‑label classification, few‑shot learning, and multimodal models.

AI PlatformsNLPmachine learning
0 likes · 11 min read
Zuoyebang’s NLP Platforms: Boosting Online Education with AI
DataFunTalk
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.

NLPindustry applicationsintent recognition
0 likes · 13 min read
A Survey of Text Classification and Intent Recognition: Industrial and Research Perspectives
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Feb 14, 2022 · Artificial Intelligence

Multimodal Evolution and Application in Tencent Game Advertising System

This article describes the end‑to‑end multimodal modeling pipeline—covering text, image, and video understanding, model evolution from shallow to deep networks, key‑frame extraction, fine‑tuning, and multimodal fusion—used in Tencent's game ad exchange platform, along with practical deployment challenges and solutions.

AdvertisingCNNMultimodal Learning
0 likes · 22 min read
Multimodal Evolution and Application in Tencent Game Advertising System
DataFunSummit
DataFunSummit
Jan 16, 2022 · Artificial Intelligence

Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation

This talk presents an overview of text‑plus‑speech multimodal emotion analysis, covering background, single‑modal text and audio models, the MSCNN‑SPU multimodal architecture, domain‑adaptation techniques, and future directions, with detailed model explanations, experimental results, and practical deployment insights.

Audio ProcessingDeep Learningmultimodal emotion analysis
0 likes · 40 min read
Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 14, 2022 · Artificial Intelligence

Boosting BERT Text Classification with Label Embedding: How It Works

The paper proposes a simple yet effective method that fuses label embeddings into BERT, enhancing text‑classification performance without increasing computational cost, and validates the approach across six benchmark datasets, also exploring tf‑idf‑based label augmentation and the impact of using [SEP] versus no‑[SEP] inputs.

BERTDeep LearningNLP
0 likes · 8 min read
Boosting BERT Text Classification with Label Embedding: How It Works
ByteDance Terminal Technology
ByteDance Terminal Technology
Jan 7, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

This article presents a comprehensive study on detecting malicious webpages by constructing heterogeneous graphs from URL redirection and textual features, applying Graph Convolutional Networks and Cluster‑Text‑GCN models, detailing optimization techniques for large‑scale deployment, and outlining future research directions.

GCNgraph neural networksheterogeneous graph
0 likes · 11 min read
Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions
Code DAO
Code DAO
Dec 12, 2021 · Artificial Intelligence

How to Boost Text Analysis Accuracy on a 2‑Billion‑Word Corpus

This article explains practical techniques for improving NLP model accuracy on massive corpora, covering challenges of multi‑field text, word‑embedding choices, a fasttext‑based regression demo with book‑review data, feature engineering tricks, and a comparison with tf‑idf + LASSO.

NLPPythonWord2Vec
0 likes · 13 min read
How to Boost Text Analysis Accuracy on a 2‑Billion‑Word Corpus
DataFunTalk
DataFunTalk
Aug 14, 2021 · Artificial Intelligence

Multimodal Advertisement Detection System for WeChat "KanKan" Articles

This article introduces a multimodal advertisement detection framework for WeChat KanKan that decomposes the problem into text, image, and article‑structure dimensions, presents novel models for ad text and image recognition, and describes how sequence classification and visualisation are used to filter severe ad‑spam articles.

Image ClassificationMultimodal AIWeChat
0 likes · 16 min read
Multimodal Advertisement Detection System for WeChat "KanKan" Articles
58 Tech
58 Tech
Aug 10, 2021 · Artificial Intelligence

Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data

This article presents a comprehensive study on extracting semantic tags from 58.com voice data, detailing the use of active learning to address cold‑start problems, comparing keyword matching, XGBoost, TextCNN, CRNN, and an improved Wide&Deep model, and demonstrating significant reductions in labeling effort and superior F1 scores across multiple experiments.

CRNNactive learningmodel comparison
0 likes · 15 min read
Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data
Ctrip Technology
Ctrip Technology
Jul 29, 2021 · Artificial Intelligence

NLP Techniques for Classifying Ctrip Ticket Customer Service Conversations

This article presents the background, problem analysis, data preprocessing, modeling approaches and optimization results of applying various NLP methods—including statistical models, word embeddings, attention mechanisms and pretrained language models such as BERT—to improve the accuracy of classifying Ctrip ticket customer service dialogues.

BERTDeep LearningNLP
0 likes · 13 min read
NLP Techniques for Classifying Ctrip Ticket Customer Service Conversations
NetEase Media Technology Team
NetEase Media Technology Team
Apr 13, 2021 · Artificial Intelligence

Applying BERT for News Timeliness Classification at NetEase

The article describes how NetEase adapts a pre‑trained BERT model to classify news articles into ultra‑short, short, or long timeliness categories by combining rule‑based strong and weak time cues, key‑sentence extraction, domain‑embedding fusion and multi‑layer semantic aggregation, achieving accurate and interpretable predictions for its platform.

BERTModel FusionNLP
0 likes · 12 min read
Applying BERT for News Timeliness Classification at NetEase
58 Tech
58 Tech
Mar 1, 2021 · Artificial Intelligence

Intelligent QABot for 58.com: Classification and Retrieval Model Exploration

This article describes how 58.com’s AI Lab built and continuously improved the QABot intelligent customer‑service system by designing classification and retrieval models, evaluating FastText, LSTM‑DSSM, BERT and a self‑developed SPTM framework, and finally fusing them to boost answer rates and user experience.

AI chatbotBERTModel Fusion
0 likes · 9 min read
Intelligent QABot for 58.com: Classification and Retrieval Model Exploration
58 Tech
58 Tech
Sep 21, 2020 · Artificial Intelligence

58.com AI Algorithm Competition: Winning Teams and Their Technical Solutions

The 58.com AI Algorithm Competition showcased intelligent customer‑service technology, with 158 teams competing on text classification and matching tasks, and the top five teams presenting detailed BERT, ELECTRA, focal‑loss and multi‑model fusion solutions along with award ceremonies, video recordings and PPT resources.

AIBERTELECTRA
0 likes · 9 min read
58.com AI Algorithm Competition: Winning Teams and Their Technical Solutions
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 28, 2020 · Artificial Intelligence

Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide

This comprehensive guide examines real‑world text classification projects, covering label taxonomy design, data scarcity solutions, efficient annotation, new‑class discovery, algorithm selection, evaluation metrics, OOV handling, model evolution, rule‑model integration, performance‑boosting tricks, and inference under resource constraints.

Few‑Shot LearningModel EvaluationNLP
0 likes · 15 min read
Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide
58 Tech
58 Tech
Aug 14, 2020 · Artificial Intelligence

Using SPTM in qa_match for the 58 City AI Competition: Data Preparation, Model Training, and Prediction

This article provides a step‑by‑step guide on preparing data, pre‑training the SPTM lightweight model, fine‑tuning a text‑classification model with qa_match, and generating competition‑ready predictions for the 58 City AI Algorithm Contest, including all required shell commands and parameter explanations.

AISPTMcompetition
0 likes · 9 min read
Using SPTM in qa_match for the 58 City AI Competition: Data Preparation, Model Training, and Prediction
58 Tech
58 Tech
Aug 12, 2020 · Artificial Intelligence

Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition

This article provides a step‑by‑step tutorial on preparing data, pre‑training the SPTM language model, fine‑tuning a text‑classification model, generating predictions, and creating a submission file for the 58.com AI algorithm competition using the open‑source qa_match toolkit.

AIModel TrainingNLP
0 likes · 9 min read
Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition
Tencent Advertising Technology
Tencent Advertising Technology
Jul 30, 2020 · Artificial Intelligence

Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM

The article details the Tencent Advertising Algorithm competition final, explains the chizhu team's approach of converting ad IDs into word sequences for text classification using large‑scale word2vec embeddings and a dual BiLSTM architecture, presents custom loss functions, training tricks, and shares full Python model code, achieving an overall rank of 11.

AdvertisingBiLSTMDeep Learning
0 likes · 9 min read
Winning Strategies for the Tencent Advertising Algorithm Competition: Text Classification with Word2Vec and BiLSTM
DataFunTalk
DataFunTalk
Jun 28, 2020 · Artificial Intelligence

Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights

This article investigates the practical performance of Google’s 2019 Unsupervised Data Augmentation (UDA) framework on real‑world financial text classification tasks, detailing experiments with limited labeled data, domain‑out‑of‑distribution samples, noisy labels, and comparisons between BERT and lightweight TextCNN models.

BERTSemi-supervised LearningTextCNN
0 likes · 21 min read
Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights
DataFunTalk
DataFunTalk
Apr 5, 2020 · Artificial Intelligence

WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation

This article describes a WeChat hotspot mining platform that integrates multiple data sources, builds quality and prediction models, employs advanced clustering and multi‑granular text matching techniques, and uses generative active learning to efficiently discover, predict, and present news hotspots for users.

WeChatactive learninghotspot detection
0 likes · 17 min read
WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 17, 2020 · Artificial Intelligence

Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform

This article describes how a BERT‑based model is fine‑tuned to compute sentence‑pair similarity for improving recommendation accuracy in an online school, detailing the architecture, training mechanisms, code implementation, experimental results, and future extensions such as sentiment analysis.

BERTChinese NLPDeep Learning
0 likes · 20 min read
Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform
Amap Tech
Amap Tech
Jan 3, 2020 · Artificial Intelligence

Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)

Amap replaced its rule‑based feedback pipeline with a three‑stage, LSTM‑driven system that combines word2vec embeddings and structured fields, achieving over 96% classification accuracy, cutting manual workload by 80%, and slashing per‑task costs while enabling scalable, data‑driven map quality improvements.

Fine-tuningGaode MapsLSTM
0 likes · 14 min read
Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)
Ctrip Technology
Ctrip Technology
Nov 21, 2019 · Artificial Intelligence

Designing and Deploying an NLP Model for Airline Ticket Customer Service

This article describes the end‑to‑end development of a multi‑class NLP system for Ctrip airline ticket customer service, covering problem analysis, data preprocessing, sample balancing, model architecture (TextCNN and Bi‑GRU), training strategies, performance evaluation, and online customization to achieve high accuracy in intent recognition.

Bi-GRUDeep LearningModel Deployment
0 likes · 16 min read
Designing and Deploying an NLP Model for Airline Ticket Customer Service
360 Tech Engineering
360 Tech Engineering
Nov 13, 2019 · Artificial Intelligence

Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform

This article introduces the Huajiao platform's text anti‑spam architecture, analyzes spam categories and challenges, compares rule‑based and machine‑learning approaches, details traditional NLP methods and the TextCNN deep‑learning model, provides its TensorFlow implementation, and describes the online deployment workflow.

CNNNLPTensorFlow
0 likes · 14 min read
Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform
Huajiao Technology
Huajiao Technology
Nov 12, 2019 · Artificial Intelligence

Text Anti‑Spam Detection with TextCNN: From Traditional Methods to Online Deployment

This article introduces the challenges of text‑based spam on the Huajiao platform, reviews traditional rule‑based and machine‑learning classification methods, explains the TextCNN architecture for robust character‑level detection, and details its TensorFlow Serving deployment for real‑time anti‑spam services.

CNNTensorFlowanti-spam
0 likes · 16 min read
Text Anti‑Spam Detection with TextCNN: From Traditional Methods to Online Deployment
Tencent Cloud Developer
Tencent Cloud Developer
Jul 19, 2019 · Artificial Intelligence

Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization

The article details a multi‑turn dialogue intent classification pipeline that extracts and expands labeled utterances, preprocesses text with custom tokenization, trains a two‑layer CNN‑Highway and a multi‑head self‑attention model, analyzes errors, and achieves up to 98.7% accuracy on a large, balanced dataset.

BERTCNNdialogue system
0 likes · 15 min read
Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization
iQIYI Technical Product Team
iQIYI Technical Product Team
May 17, 2019 · Artificial Intelligence

Kui: AI-Powered Anti-Spam System Architecture and Strategies

Kui is iQiyi’s AI‑driven anti‑spam platform that protects online communities through a three‑layer architecture—service, algorithm strategy, and auxiliary modules—and employs keyword, rule‑based, machine‑learning, and risk‑control strategies to detect advertising, pornographic, abusive and other malicious content while continuously adapting to evolving threats.

AI systemanti-spamcontent moderation
0 likes · 10 min read
Kui: AI-Powered Anti-Spam System Architecture and Strategies
58 Tech
58 Tech
Feb 22, 2019 · Artificial Intelligence

Algorithm Evolution and Implementation of 58.com Intelligent QABot for Business Consultation

The article details the design and iterative improvement of 58.com’s intelligent QABot, covering knowledge‑base construction, feature engineering, three generations of classification models—including FastText, Bi‑LSTM, and deep semantic matching—and evaluation metrics that achieve high accuracy and automation rates.

AIDeep LearningIntelligent Customer Service
0 likes · 12 min read
Algorithm Evolution and Implementation of 58.com Intelligent QABot for Business Consultation
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 25, 2019 · Artificial Intelligence

Multimodal Video Quality Assessment Models for Short Video Platforms

The paper presents an integrated multimodal quality assessment system for short‑video platforms that evaluates cover images, video content, and accompanying text using deep‑learning and handcrafted features—combining ResNet‑50, NetVLAD, TSN, VGGish, and XGBoost—to improve user experience, recommendation accuracy, and operational efficiency, with plans for optimization and modular deployment.

Image AnalysisMultimodal Learningtext classification
0 likes · 11 min read
Multimodal Video Quality Assessment Models for Short Video Platforms
DataFunTalk
DataFunTalk
Jan 9, 2019 · Artificial Intelligence

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

This article introduces reinforcement learning fundamentals, contrasts it with supervised learning, and explores its challenges and advantages in natural language processing, including applications such as text classification, relation extraction from noisy data, and weakly supervised topic segmentation, while summarizing key insights and experimental results.

Weak Supervisionnatural language processingreinforcement learning
0 likes · 11 min read
Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications
AntTech
AntTech
Aug 16, 2018 · Artificial Intelligence

Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection

This article reviews deep‑learning‑based text classification techniques—including TextCNN, BiGRU, Capsule Networks, Attention mechanisms, and the novel cw2vec embedding—applied to Alipay complaint fraud data, presents experimental comparisons, and discusses their advantages, challenges, and future directions.

AlipayDeep Learningattention
0 likes · 18 min read
Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection
Tencent Cloud Developer
Tencent Cloud Developer
Mar 21, 2018 · Artificial Intelligence

Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach

The article proposes a hybrid approach that first filters blacklist words and then classifies suspicious comments with a character-level TextCNN, achieving around 89% precision and 87% recall, demonstrating that simple convolutional networks outperform keyword filters and RNNs for short, noisy abusive Chinese text.

Abusive Comment DetectionDeep LearningNLP
0 likes · 10 min read
Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Mar 6, 2018 · Artificial Intelligence

Master Naive Bayes: From Theory to Python Text Classification

This article introduces the Naive Bayes classifier, explains its underlying probability formulas—including conditional probability, total probability, and the Bayes theorem—covers the feature independence assumption, Laplace smoothing, and demonstrates both manual and scikit‑learn implementations for email and text classification with Python code.

Naive Bayesprobabilityscikit-learn
0 likes · 11 min read
Master Naive Bayes: From Theory to Python Text Classification
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 15, 2017 · Artificial Intelligence

Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment

The team built a lightweight three‑class sentiment classifier for iQIYI user comments using a linear‑kernel SVM with high‑dimensional bag‑of‑words features and an expanded ~100k word lexicon, achieving over 96% accuracy across domains, and deployed it as a Spring Boot PMML service with zero‑downtime refresh, while planning GBDT‑enhanced features and word‑embedding optimizations.

DeploymentNLPSentiment Analysis
0 likes · 13 min read
Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment
Meituan Technology Team
Meituan Technology Team
Oct 12, 2017 · Artificial Intelligence

Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More

The article answers ten machine‑learning questions, explaining how to impute missing behavior data, extract and select features, describe Meituan‑Dianping’s recommendation pipeline, suggest a beginner learning path, clarify L1 sparsity, recommend TextCNN for text, discuss search‑ranking sample bias, label generation for wide‑deep models, the shift to deep‑learning video detection, and the use of factorization machines for CTR with open‑source examples.

Deep LearningL1 RegularizationRecommendation Systems
0 likes · 7 min read
Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More
Qunar Tech Salon
Qunar Tech Salon
Aug 18, 2016 · Artificial Intelligence

Automatic Ticket Classification Using SVM and word2vec at Qunar

At Qunar, the data center algorithm team developed an automatic ticket classification system that combines Support Vector Machine with word2vec embeddings to handle high‑dimensional, low‑sample text data, achieving 89% accuracy and 80% recall while outlining the full machine‑learning pipeline from feature extraction to deployment.

QunarWord2Vecmachine learning
0 likes · 7 min read
Automatic Ticket Classification Using SVM and word2vec at Qunar
21CTO
21CTO
Feb 12, 2016 · Artificial Intelligence

Can Machine Learning Reveal the True Author of Red Mansions' Final 40 Chapters?

This article uses machine learning to compare lexical patterns between the first 80 and last 40 chapters of 'Dream of the Red Chamber', demonstrating distinct stylistic differences that support the scholarly view that the final chapters were not authored by Cao Xueqin.

Red MansionsSupport Vector Machinefeature engineering
0 likes · 6 min read
Can Machine Learning Reveal the True Author of Red Mansions' Final 40 Chapters?
Suning Technology
Suning Technology
Jun 18, 2015 · Artificial Intelligence

How Suning Uses Naive Bayes for High‑Accuracy Product Classification

This article explains Suning's implementation of a Naive Bayes‑based product classification system, detailing its basic theory, formal definition, step‑by‑step training process, three implementation phases, evaluation results, and error analysis to improve classification accuracy.

Naive BayesSuningalgorithm
0 likes · 6 min read
How Suning Uses Naive Bayes for High‑Accuracy Product Classification
Meituan Technology Team
Meituan Technology Team
Dec 18, 2014 · Artificial Intelligence

Auto-Label Missing POI Categories Using Naive Bayes and Feature Selection

This article details a step‑by‑step machine‑learning pipeline that transforms over one million calibrated POI records into feature vectors, selects discriminative terms via information‑gain and domain rules, trains a Naive Bayes classifier, and achieves 91% accuracy with 84% coverage on unseen POI data.

Chinese NLPNaive BayesPOI classification
0 likes · 12 min read
Auto-Label Missing POI Categories Using Naive Bayes and Feature Selection