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Entity Recognition

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DataFunSummit
DataFunSummit
Jul 17, 2023 · Artificial Intelligence

Introduction to ModelScope Community's Fundamental NLP Models and Their Applications

This article introduces the ModelScope community's suite of foundational NLP models—including tokenization, POS tagging, NER, and text representation—detailing their architectures, performance, application scenarios, while also highlighting research contributions such as the ACE framework and retrieval‑enhanced techniques.

Artificial IntelligenceEntity RecognitionModelScope
0 likes · 21 min read
Introduction to ModelScope Community's Fundamental NLP Models and Their Applications
DataFunTalk
DataFunTalk
Nov 21, 2022 · Artificial Intelligence

Research on Information Extraction from a Graph Perspective

This presentation reviews the background, significance, current research status, objectives, and key contributions of a graph‑based approach to information extraction, covering entity recognition, relation extraction, event extraction, open‑domain extraction, and the proposed unified modeling framework with experimental results.

Entity RecognitionInformation ExtractionNLP
0 likes · 27 min read
Research on Information Extraction from a Graph Perspective
DataFunTalk
DataFunTalk
Apr 29, 2022 · Artificial Intelligence

Knowledge‑Enhanced Product Understanding with Meituan Brain: Building and Applying a Large‑Scale Product Knowledge Graph

This talk presents Meituan Brain's massive product knowledge graph, explains how knowledge‑enhanced models improve product title parsing, category association and sample governance, and demonstrates the resulting gains in search, recommendation and other downstream services while keeping the system online‑controllable and explainable.

AIEntity RecognitionMeituan
0 likes · 22 min read
Knowledge‑Enhanced Product Understanding with Meituan Brain: Building and Applying a Large‑Scale Product Knowledge Graph
Python Programming Learning Circle
Python Programming Learning Circle
Jan 12, 2022 · Artificial Intelligence

Building a Streamlit Web Application for NLP Tasks: Sentiment Analysis, Entity Extraction, and Text Summarization

This tutorial demonstrates how to create a lightweight Streamlit web app in Python that lets users select and run common NLP services—sentiment analysis, named‑entity recognition, and text summarization—by integrating libraries such as TextBlob, spaCy, and Gensim, with clear code examples and visual output.

Entity RecognitionNLPStreamlit
0 likes · 13 min read
Building a Streamlit Web Application for NLP Tasks: Sentiment Analysis, Entity Extraction, and Text Summarization
58 Tech
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
DataFunTalk
DataFunTalk
Nov 15, 2020 · Artificial Intelligence

Query Intent Recognition in Vertical Search: Challenges, Methods, and Case Studies

The article reviews the importance of query intent recognition in vertical search, outlines its definition, highlights practical challenges such as ambiguous input, multi‑intent queries, timeliness and cold‑start issues, and surveys common rule‑based, statistical, and machine‑learning solutions together with real‑world case studies.

Entity RecognitionNLUSearch
0 likes · 17 min read
Query Intent Recognition in Vertical Search: Challenges, Methods, and Case Studies
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
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 22, 2017 · Artificial Intelligence

iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services

The iQIYI NLP team applies lexical analysis, knowledge‑graph construction, tag recommendation, query understanding, voice‑assistant semantics, sentiment mining, and box‑office/view‑count prediction—leveraging weakly labeled data, CRF/CNN‑CRF models and deep learning—to enhance video comprehension, recommendation, search and commercial services across the platform.

Entity RecognitionNLPRecommendation systems
0 likes · 13 min read
iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services
Ctrip Technology
Ctrip Technology
Aug 28, 2017 · Artificial Intelligence

Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases

This article examines the construction, reasoning, and large‑scale applications of knowledge graphs, discussing graph building techniques, storage solutions, deep‑learning‑based entity extraction, inference models such as TransR and RESCAL, and how these graphs enhance search, recommendation, and other AI systems.

Deep LearningEntity RecognitionNatural Language Processing
0 likes · 13 min read
Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases
Ctrip Technology
Ctrip Technology
Jul 29, 2016 · Artificial Intelligence

Knowledge Graph Based Question Answering System: Architecture, Research Results, and Deep Learning Approaches

This article presents a knowledge‑graph‑driven question answering system, detailing its three‑layer architecture, semantic search and disambiguation techniques, verb‑semantic templates, deep‑learning models, experimental results, and current challenges in data quality and model integration.

Artificial IntelligenceDeep LearningEntity Recognition
0 likes · 7 min read
Knowledge Graph Based Question Answering System: Architecture, Research Results, and Deep Learning Approaches