0 views collected around this technical thread.
This tutorial introduces AIGC concepts and walks through practical implementations of tokenization, part‑of‑speech tagging, and named entity recognition using the Transformers library, NLTK, and spaCy on Google Colab, complete with code snippets and visual results.
This article introduces VentureSights, an AI‑driven intelligent industry analysis platform built on knowledge‑graph technology and the concept of industry atoms, detailing its core modules, workflow, industry‑atom representation, extraction algorithms, and overall system architecture for generating comprehensive industry reports and insights.
This article examines the robustness challenges of information extraction tasks such as NER and relation extraction, introduces the Entity Coverage Ratio metric, analyzes why pretrained models like BERT may “take shortcuts,” and proposes evaluation tools and training strategies—including mutual‑information‑based methods, negative‑training, and flooding—to improve model robustness across diverse scenarios.
This technical report presents a comprehensive overview of artificial intelligence evolution, knowledge‑graph construction techniques—including traditional, cross‑lingual and reading‑comprehension based named entity recognition, weak‑supervised and joint relation extraction, attribute completion via multi‑source cues, and conditional knowledge‑graph modeling—highlighting recent research findings and experimental results.
This article presents Didi's voice interaction platform, detailing the natural language understanding pipeline, ASR error correction methods, intent classification strategies, and named entity recognition models, while discussing practical deployments, performance gains, and future research directions.
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.
Didi’s voice‑interaction platform combines a three‑stage ASR error‑correction pipeline, optimized intent‑classification models (both end‑to‑end and retrieval‑based), and advanced Chinese NER using Bi‑GRU‑CRF and BERT‑CRF, boosting transcription accuracy and overall dialogue success while supporting scalable deployment and future enhancements such as lattice inputs and richer acoustic signals.
This article presents a comprehensive study on slot (entity) recognition and error correction for voice robots, describing the labeling scheme, data annotation, IDCNN+CRF and BiLSTM+CRF models, a pinyin‑based similarity algorithm, and reporting significant accuracy improvements in real‑world deployments.
The article details Ctrip’s Marco Polo content platform, describing its data, algorithm, and functional layers, and explains how AI techniques such as NLP, semantic matching, named‑entity recognition, and image classification are applied to automate product‑centric content mining, article generation, quality rating, and first‑image selection.
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.