TextCNN | BestHub

Airbnb Technology Team

Apr 15, 2024 · Artificial Intelligence

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

Airbnb’s Attribute Prioritization System uses a machine‑learning pipeline called LATEX to extract and map guest‑mentioned amenities, activities and places from reviews, messages and tickets, then predicts and ranks the most important attributes per listing, giving hosts personalized suggestions to improve listings and match traveler needs.

AirbnbNERNLP

0 likes · 9 min read

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

58 Tech

Jan 27, 2021 · Artificial Intelligence

Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot

This article details the BangBang intelligent customer service system's overall architecture, core capabilities, knowledge‑base construction, and successive model upgrades—from FastText to TextCNN, Bi‑LSTM, and model fusion—showing how each iteration improved accuracy, recall, and F1 scores toward a stable 95% performance level.

AILSTMModel Fusion

0 likes · 12 min read

Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot

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%.

Cold StartTextCNNXGBoost

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

58 Tech

Oct 9, 2020 · Artificial Intelligence

Speaker Role Recognition in an Intelligent Voice Analysis Platform

This article describes a speaker role recognition system for a voice analysis platform, detailing a gender‑based pre‑filter, keyword‑matching and TextCNN‑based text classification, and single‑sentence correction methods that together improve role assignment accuracy by about 6% over baseline third‑party solutions.

AINLPTextCNN

0 likes · 12 min read

Speaker Role Recognition in an Intelligent Voice Analysis Platform

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 LearningText Classification

0 likes · 21 min read

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

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

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

Amap Tech

Aug 27, 2019 · Artificial Intelligence

POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design

The system tackles POI category tagging as a multi‑label classification problem by engineering textual and non‑textual features, mining click‑log and external samples through active learning, and deploying hierarchical and per‑tag deep textCNN models with feature fusion, achieving over 5 % accuracy gain, ten‑fold speedup, and markedly higher precision and coverage that boost map‑search relevance.

Feature EngineeringPOI taggingSearch Relevance

0 likes · 19 min read

POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design

JD Tech Talk

Jan 10, 2019 · Artificial Intelligence

Sensitive Field Identification Using Wide & Deep and TextCNN Models

This article presents a machine‑learning approach for detecting sensitive data fields in a data warehouse by combining a Wide & Deep network with a TextCNN architecture, detailing data exploration, model design, training strategies, performance results, and deployment workflow.

Feature EngineeringSensitive Data DetectionTextCNN

0 likes · 9 min read

Sensitive Field Identification Using Wide & Deep and TextCNN Models

AntTech

Jun 27, 2018 · Artificial Intelligence

Cross-Domain Review Helpfulness Prediction Using CNN with Auxiliary Domain Discriminators

This paper presents an end‑to‑end approach that combines an improved TextCNN with character‑level embeddings and a specific‑shared adversarial transfer‑learning framework to predict the helpfulness of e‑commerce reviews, demonstrating superior performance especially when target‑domain labeled data are scarce.

Natural Language ProcessingTextCNNcross-domain transfer learning

0 likes · 12 min read

Cross-Domain Review Helpfulness Prediction Using CNN with Auxiliary Domain Discriminators

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