This article explores how Ctrip leverages advanced AI techniques—including named entity recognition, entity linking, large language models, and vector search—to replace traditional keyword queries with semantic search, dramatically improving hotel recommendation accuracy and user experience.
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.
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.
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.
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.
This article explores how table‑based question answering can be integrated into B2B intelligent customer service by converting table queries into entity‑attribute recognition and multi‑turn dialogue, comparing end‑to‑end NL2SQL and slot‑filling approaches, and presenting NetEase Qiyu's practical implementation with its benefits and use cases.
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.
This article presents a comprehensive overview of Meituan's query understanding system, detailing how entity recognition, entity linking, query rewriting, and intent detection are designed and deployed to improve O2O search relevance across diverse local services.
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.
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.
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.
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.
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.
