This guide introduces Stanford CoreNLP, a Python interface for fundamental NLP tasks such as tokenization, part‑of‑speech tagging, named‑entity recognition, constituency and dependency parsing, showing installation steps, model download links, and example outputs.
This article provides concise answers to frequent Natural Language Processing questions, covering the distinction between NLP and NLG, popular pretrained models, deep‑learning architectures, word‑vector techniques, named‑entity recognition, sentiment, semantic and syntax analysis, part‑of‑speech tagging, language models, core tasks, real‑world applications, challenges, future trends, interpretability, and essential tools and libraries.
The article explains how using plain prompts to force large language models to output JSON for intent recognition and named‑entity extraction leads to unpredictable extra text, key mismatches, and hallucinated fields, and it presents three robust alternatives—placeholders, function calling, and constrained decoding—to achieve reliable structured outputs.
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.
SpanProto, a two‑stage span‑based prototypical network, dramatically improves few‑shot named entity recognition by extracting candidate spans with a global boundary matrix and classifying them via prototypical and margin learning, achieving notable gains on the Few‑NERD benchmark with minimal labeled data.
This article reviews the recent NLPCC 2022 NER competition, explains the evolution of named entity recognition, details the five major modeling paradigms, and describes the winning team’s relation‑classification approach, data‑augmentation strategy, experimental results, and its practical deployment in NetEase Cloud Commerce services.
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.
This article reviews Alibaba’s year‑long Knowledge Engine program, detailing its five‑module architecture, major technical breakthroughs such as automatic ontology building and deep‑learning alignment, and two flagship research works: adversarial learning for crowdsourced NER and an iterative rule‑and‑embedding reasoning framework.
This paper proposes a Chinese named entity recognition method that leverages noisy crowdsourced annotations through adversarial training with dual BiLSTM modules, demonstrating consistent F1 improvements on dialogue and e‑commerce datasets.
