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Jul 21, 2023 · Artificial Intelligence

Problems and Solutions in Semantic Segmentation: An Overview of DeepLabV1

This article explains the two main challenges of applying deep convolutional neural networks to semantic segmentation—signal down‑sampling and loss of spatial precision—and describes how the DeepLabV1 architecture, using dilated convolutions, large‑field‑of‑view modules, fully‑connected CRF and multi‑scale fusion, addresses these issues while achieving faster, more accurate segmentation results.

CRFDeep LearningDeepLabV1

0 likes · 12 min read

Problems and Solutions in Semantic Segmentation: An Overview of DeepLabV1

58 Tech

Aug 19, 2021 · Artificial Intelligence

Practical NER Techniques for Business Chatbots on the 58.com Service Platform

This article presents a comprehensive case study of applying named‑entity‑recognition (NER) techniques to the smart chat assistant of 58.com’s yellow‑page service, covering business background, model selection (BiLSTM‑CRF, IDCNN‑CRF, BERT), data‑augmentation, focal loss, fusion of rule‑based and neural methods, context modeling, online performance, and future research directions.

BERTCRFEntity Recognition

0 likes · 16 min read

Practical NER Techniques for Business Chatbots on the 58.com Service Platform

58 Tech

May 24, 2021 · Artificial Intelligence

Tag Extraction for 58 Yellow Pages Posts Using Sequence Labeling and Model Optimization

This article describes a complete solution for extracting and normalizing tags from 58 Yellow Pages service posts, covering candidate word acquisition, sequence‑labeling models such as CRF and BERT‑CRF, hierarchical softmax optimization for massive label spaces, and experimental results on both post content and user reviews.

BERTCRFhierarchical softmax

0 likes · 20 min read

Tag Extraction for 58 Yellow Pages Posts Using Sequence Labeling and Model Optimization

58 Tech

Jan 29, 2021 · Artificial Intelligence

Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

This article details the background, challenges, architecture, model selection, and future directions of the business‑opportunity slot recognition module used in 58.com’s intelligent customer service, highlighting how regex‑model fusion and IDCNN‑CRF improve entity extraction for phone, WeChat, address, and time slots.

BERTCRFIDCNN

0 likes · 11 min read

Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

DataFunSummit

Dec 27, 2020 · Artificial Intelligence

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

This article provides a comprehensive overview of sequence labeling tasks in NLP, covering their definition, common tag schemes (BIO, BIEO, BIESO), comparisons with other NLP tasks, major modeling approaches such as HMM, CRF, RNN and BERT, real‑world applications like POS tagging, NER, event extraction and gene analysis, and a step‑by‑step PyTorch implementation with dataset preparation, training pipeline, and evaluation metrics.

BERTCRFHMM

0 likes · 27 min read

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

JD Tech Talk

Dec 3, 2020 · Artificial Intelligence

Consumer Behavior Cause Extraction with BERT Fine‑tuning and a Novel Sequence‑Labeling Framework (ICDM 2020 Winning Solution)

At ICDM 2020, the JD Digits Silicon Valley team achieved top results in the Knowledge Graph Contest by fine‑tuning BERT and introducing a novel sequence‑labeling framework that jointly extracts consumer behavior types and their underlying reasons, leveraging CRF decoding and model ensemble for superior performance.

BERTCRFICDM 2020

0 likes · 11 min read

Consumer Behavior Cause Extraction with BERT Fine‑tuning and a Novel Sequence‑Labeling Framework (ICDM 2020 Winning Solution)

HomeTech

Nov 13, 2019 · Artificial Intelligence

Sequence Labeling for Entity Recognition in Automotive Search: Techniques and Applications

This article examines how sequence labeling methods such as pattern matching, CRF, and deep‑learning models like BiLSTM‑CRF and BERT are applied to automotive search tasks—including car‑series, model, and location/entity recognition—detailing their development, implementation challenges, and performance results.

AutomotiveBERTCRF

0 likes · 11 min read

Sequence Labeling for Entity Recognition in Automotive Search: Techniques and Applications

58 Tech

May 31, 2019 · Artificial Intelligence

Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT

This article reviews modern deep‑learning methods for Chinese word segmentation, comparing traditional CRF‑based approaches with BiLSTM‑CRF and BERT models, describing their architectures, training procedures, experimental results, and practical considerations for deployment.

BERTBiLSTMCRF

0 likes · 17 min read

Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT

Sohu Tech Products

Apr 11, 2019 · Artificial Intelligence

Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation

This article reviews the challenges of media‑domain named entity recognition, outlines the evolution from rule‑based methods through traditional machine‑learning and deep‑learning models to attention‑based Transformers, and details Sohu’s practical Bi‑LSTM‑CRF system with data‑annotation strategies and performance results.

Bi-LSTMCRFDeep Learning

0 likes · 12 min read

Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation

58 Tech

Jan 22, 2019 · Artificial Intelligence

Chinese Word Segmentation: Challenges, Methods, and Practical Practices

The article explains why Chinese word segmentation is essential for NLP tasks, outlines its fundamental difficulties such as ambiguity and out‑of‑vocabulary words, reviews dictionary‑based, statistical, and CRF approaches, and shares practical experiences from 58 Search’s production system.

CRFChinese segmentationNLP

0 likes · 21 min read

Chinese Word Segmentation: Challenges, Methods, and Practical Practices