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58 Tech
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
DataFunSummit
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
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
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
DataFunTalk
DataFunTalk
Jun 10, 2019 · Artificial Intelligence

BERT Applications Across NLP Domains: Progress, Challenges, and Future Directions

This article surveys the rapid proliferation of BERT-based research over the past six months, analyzing its impact on various NLP tasks such as question answering, information retrieval, dialog systems, summarization, data augmentation, classification, and sequence labeling, while also discussing the model's strengths, limitations, and future research opportunities.

BERTNLPText Summarization
0 likes · 52 min read
BERT Applications Across NLP Domains: Progress, Challenges, and Future Directions