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This article describes how a commercial search platform iteratively upgrades its system by structuring business knowledge into a knowledge graph, applying multi‑stage entity extraction (CRF, Electra‑CRF, GLM‑3, OCR), and leveraging large language models to improve relevance, user experience, and revenue.
Airbnb’s Listing Attribute Extraction Platform (LAEP) uses a custom NER model, word‑embedding mapping, and a BERT‑based scorer to automatically pull, normalize, and validate structured attributes from hosts’ unstructured text, boosting coverage for downstream tools and enhancing guest‑host matching at scale.
This article presents the business background of Tmall Genie’s voice‑driven content‑on‑demand service, critiques the traditional pipeline for entity extraction, and details an end‑to‑end speech‑semantic model—including the Speech2Slot architecture, knowledge‑enhanced encoding, and Phoneme‑BERT unsupervised pre‑training—demonstrating significant performance gains in both generation and classification tasks.
The article presents Tmall Genie’s end‑to‑end speech‑semantic understanding pipeline, detailing the limitations of traditional ASR‑NLU‑IR pipelines, introducing the Speech2Slot model with knowledge‑enhanced encoders, and describing unsupervised phoneme‑based pre‑training (Phoneme‑BERT) that improves entity extraction performance in voice‑driven content playback.
This article presents Alibaba's AliMe team’s year‑long progress on knowledge graphs, covering the basics of knowledge graphs, domain‑specific and multi‑modal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and key takeaways for future research.
This article presents Alibaba's AliMe team's year‑long progress on knowledge graph research, covering the fundamentals of knowledge graphs, domain and multimodal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and insights on future directions.
This article reviews Alibaba Cloud senior algorithm expert Chen Mosha's presentation on medical NLP, covering Alibaba's healthcare business, data types, electronic medical record quality inspection, span‑based and nested NER models, term normalization, clinical trial outcome prediction, knowledge‑enhanced language models, and the CBLUE benchmark dataset.
iQIYI’s Qisou Knowledge Graph, built since 2015 through a five‑stage pipeline of schema modeling, multi‑source data acquisition, entity alignment fusion, JanusGraph‑HBase storage, and inference‑driven querying, now powers precise video search, intelligent Q&A, tag mining, and relationship‑based recommendation across its platform.
This article describes how 58.com built an end‑to‑end bidding service that crawls tender documents, classifies them into multiple categories with BERT‑based models (including softmax, sigmoid and ensemble approaches) and extracts key entities using BERT‑CRF and reading‑comprehension techniques, achieving over 90% overall accuracy and dramatically improving recall and precision.
This article details the background, challenges, architecture, model selection, and future directions of the business‑opportunity slot recognition module used in 58.com’s intelligent customer service, highlighting how regex‑model fusion and IDCNN‑CRF improve entity extraction for phone, WeChat, address, and time slots.
This article explains how financial institutions can leverage large amounts of structured and unstructured data to build and apply financial knowledge graphs, covering AI key technologies, schema design, data extraction, graph construction, storage solutions, and real-world use cases such as intelligent tagging, recommendation, policy analysis, and executive relationship mining.
This article explains how Alibaba B2B leverages knowledge‑graph technology—from its historical roots in knowledge engineering and expert systems to modern semantic‑web models, extraction pipelines, reasoning methods, storage solutions, and representation learning—to improve search, recommendation, and scene‑based procurement incentives in e‑commerce platforms.
This article provides a comprehensive QA‑style review of entity‑relation extraction (ERE), covering pipeline drawbacks, various decoding strategies for NER, common relation‑classification techniques, shared‑parameter and joint‑decoding models, recent transformer‑based approaches, challenges such as overlapping entities, low‑resource settings, and the use of graph neural networks.
The talk introduced iQIYI’s HomeAI platform, which combines user profiling (including voiceprint and age detection) with automatic video semantic extraction to enable natural, multi‑turn voice‑based video search—addressing hot‑content updates, contextual awareness, device environments, and personalized recommendations for screen‑less or accessibility‑focused users.
The article presents TaskBot, a modular task‑oriented dialogue robot that uses a Bi‑LSTM‑CRF joint intent‑entity model and a state‑machine based dialogue manager to handle multi‑turn conversations such as flight booking or rental housing, detailing its architecture, implementation, and performance.
This article explains how a knowledge graph for second‑hand e‑commerce is built—from data extraction and entity, attribute, and relation mining to ontology construction, entity alignment, and graph integration—and describes how the resulting graph supports personalized recommendation, search optimization, and statistical or regression‑based pricing models.