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535 articles · Page 5 of 6
DataFunTalk
DataFunTalk
May 7, 2020 · Artificial Intelligence

Comprehensive Overview of Query Understanding in Search Engines

Query understanding (QU) involves lexical, syntactic, and semantic analysis of user queries to enable effective search recall and ranking, covering modules such as preprocessing, correction, expansion, segmentation, intent detection, term importance, and guidance, with detailed discussion of algorithms, models, and system architecture.

NLPinformation retrievalmachine learning
0 likes · 51 min read
Comprehensive Overview of Query Understanding in Search Engines
Ctrip Technology
Ctrip Technology
Apr 30, 2020 · Artificial Intelligence

Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions

This article presents a comprehensive overview of automated techniques for generating high‑quality search engine advertising keywords, covering background, traditional manual methods, intelligent keyword expansion using NLP, segmentation, POS tagging, BILSTM‑CRF, BERT classification, semantic matching with DSSM, and additional approaches such as query suggestion and synonym rewriting.

BERTBILSTM-CRFNLP
0 likes · 15 min read
Intelligent Generation of Search Engine Advertising Keywords: Methods, Frameworks, and Future Directions
Yanxuan Tech Team
Yanxuan Tech Team
Apr 20, 2020 · Artificial Intelligence

How AI-Driven Clustering Boosts Smart Customer Service Knowledge Bases

This article outlines an AI-powered workflow for constructing and enriching a business knowledge base in intelligent customer service, covering preprocessing, intent detection, deep and shallow semantic feature engineering, hierarchical bucket clustering, and automated summary extraction to improve FAQ coverage and reduce manual workload.

AIClusteringCustomer Service
0 likes · 15 min read
How AI-Driven Clustering Boosts Smart Customer Service Knowledge Bases
DataFunTalk
DataFunTalk
Apr 10, 2020 · Artificial Intelligence

Improving Machine Translation: Addressing Exposure Bias, Efficient Decoding, and Non‑Autoregressive Models

This article reviews recent research on machine translation that tackles the training‑inference distribution gap, exposure bias, and slow autoregressive decoding by introducing scheduled sampling, differentiable sequence‑level losses, cube‑pruning, and sequence‑aware non‑autoregressive decoding, showing BLEU gains and significant speedups.

BLEUNLPcube pruning
0 likes · 16 min read
Improving Machine Translation: Addressing Exposure Bias, Efficient Decoding, and Non‑Autoregressive Models
Didi Tech
Didi Tech
Apr 2, 2020 · Artificial Intelligence

Interview: Didi AI’s DELTA – A Unified Framework for NLP and Speech Model Development

In this interview, Didi AI Labs’ Han Kun explains how the DELTA platform unifies TensorFlow‑based NLP and speech models—supporting tasks from text classification to voice emotion recognition—through a modular, easily deployable architecture, accelerating development, powering Didi products, and now open‑sourced for broader AI collaboration.

AI platformDeltaNLP
0 likes · 14 min read
Interview: Didi AI’s DELTA – A Unified Framework for NLP and Speech Model Development
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 27, 2020 · Artificial Intelligence

Multimodal Short Video Content Tagging Techniques and Applications at iQIYI

The article surveys iQIYI’s multimodal short‑video content‑tagging pipeline, detailing extraction‑ and generation‑based methods, challenges of open‑world tags, model evolution from rule‑based to Transformer generators, visual‑text fusion techniques, and applications such as recommendation, search, clustering, and future enhancements.

MultimodalNLPcontent tagging
0 likes · 18 min read
Multimodal Short Video Content Tagging Techniques and Applications at iQIYI
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 26, 2020 · Artificial Intelligence

How Amap Uses AI to Automate Millions of User Feedback Reports

This article describes how Gaode Map leverages machine‑learning techniques—such as word2vec embeddings, LSTM networks, fine‑tuning, and confidence‑threshold ensembles—to automatically classify and verify massive user‑feedback intelligence, streamlining the multi‑step workflow from data collection to road‑map updates and dramatically improving efficiency.

AILSTMNLP
0 likes · 16 min read
How Amap Uses AI to Automate Millions of User Feedback Reports
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 24, 2020 · Artificial Intelligence

How Knowledge Graphs and GNNs Boost HS Code Classification Accuracy

This article explores how integrating unstructured business data into structured knowledge graphs and applying graph neural networks can overcome deep‑learning bottlenecks in NLP, dramatically improving HS‑code product classification accuracy from around 60% to over 75% through richer reasoning and multimodal knowledge.

AIGNNGraph Neural Network
0 likes · 20 min read
How Knowledge Graphs and GNNs Boost HS Code Classification Accuracy
DataFunTalk
DataFunTalk
Mar 22, 2020 · Artificial Intelligence

Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances

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.

NLPdeep learningentity extraction
0 likes · 32 min read
Entity and Relation Extraction: QA-Style Overview of Methods, Challenges, and Recent Advances
DataFunTalk
DataFunTalk
Mar 17, 2020 · Artificial Intelligence

A Survey of Text Data Augmentation Techniques in Natural Language Processing

This article systematically reviews recent developments in text data augmentation for natural language processing, covering common scenarios such as low‑resource and imbalanced classification, and detailing five major techniques—including back‑translation, EDA, TF‑IDF‑based replacement, contextual augmentation, and language‑model‑based methods—with experimental results and future directions.

Data AugmentationNLPTransfer Learning
0 likes · 27 min read
A Survey of Text Data Augmentation Techniques in Natural Language Processing
58 Tech
58 Tech
Mar 2, 2020 · Artificial Intelligence

Low-Quality Text Detection Using Unsupervised Language Model Perplexity

This article proposes a method to identify low-quality text in business data by training a large-scale unsupervised language model to compute sentence perplexity, converting the detection problem into a threshold decision, and details model design, challenges, optimizations, and online performance results.

BERTNLPTransformer
0 likes · 13 min read
Low-Quality Text Detection Using Unsupervised Language Model Perplexity
58 Tech
58 Tech
Feb 26, 2020 · Artificial Intelligence

Tag Mining and Optimization Practices Using Chinese Segmentation Tools

This article presents a comprehensive overview of tag mining practices—including similarity‑based, compound‑word, topic, hot‑search, and image‑based approaches—along with detailed evaluations of Chinese segmentation tools and systematic tag optimization techniques such as synonym and negative‑word detection.

HanLPNLPchinese segmentation
0 likes · 15 min read
Tag Mining and Optimization Practices Using Chinese Segmentation Tools
DataFunTalk
DataFunTalk
Feb 24, 2020 · Artificial Intelligence

Adversarial Training for Transformer‑Based Natural Language Models: Methods, Variants, and Experimental Results

This presentation reviews adversarial training techniques for transformer‑based NLP models, covering the motivation, image‑based and text‑based attack generation, standard PGD, its variants FreeAT and YOPO, the proposed FreeLB method, extensive GLUE experiments, and conclusions about robustness and future directions.

FreeLBNLPRobustness
0 likes · 18 min read
Adversarial Training for Transformer‑Based Natural Language Models: Methods, Variants, and Experimental Results
DataFunTalk
DataFunTalk
Feb 18, 2020 · Artificial Intelligence

Why Natural Language Understanding Is Difficult: Structure Prediction, Semantic Representation, and Multimodal Context

The article explains that natural language understanding is fundamentally a structure‑prediction problem whose difficulty stems from language innovation, recursion, ambiguity, subjectivity and social factors, and argues that richer semantic representation spaces and multimodal context modeling are needed for true AI comprehension.

Multimodal ContextNLPSemantic Representation
0 likes · 20 min read
Why Natural Language Understanding Is Difficult: Structure Prediction, Semantic Representation, and Multimodal Context
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2020 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs

The article explains how Tencent tackles content understanding in feed recommendation by evolving from traditional classification, keyword, and entity methods to a multi‑layer interest graph that captures concepts and events, addressing the need for full context, reasoning about user intent, and improving online performance.

AIEmbeddingNLP
0 likes · 12 min read
Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs
Ctrip Technology
Ctrip Technology
Jan 16, 2020 · Artificial Intelligence

Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization

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.

AICtripNLP
0 likes · 16 min read
Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization
Amap Tech
Amap Tech
Jan 3, 2020 · Artificial Intelligence

Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)

Amap replaced its rule‑based feedback pipeline with a three‑stage, LSTM‑driven system that combines word2vec embeddings and structured fields, achieving over 96% classification accuracy, cutting manual workload by 80%, and slashing per‑task costs while enabling scalable, data‑driven map quality improvements.

Fine-tuningGaode MapsLSTM
0 likes · 14 min read
Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)
DataFunTalk
DataFunTalk
Jan 2, 2020 · Artificial Intelligence

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

This article details Zhihu's search engineering advances over the past year, covering long‑tail query challenges, term‑weight calculation, synonym expansion, query rewriting with translation models and reinforcement learning, and semantic retrieval using BERT‑based embeddings, while outlining future research directions.

NLPQuery Rewritingquery understanding
0 likes · 14 min read
Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval
DataFunTalk
DataFunTalk
Dec 27, 2019 · Artificial Intelligence

NLP Challenges and Tagging Solutions in Sina Weibo Feed

This article reviews the specific NLP difficulties encountered in Sina Weibo's feed—such as short text, informal language, and ambiguous user behavior—and details the multi‑stage tagging system, material library, multimodal modeling, multi‑task learning, and large‑scale pre‑training techniques used to address them.

BERTNLPWeibo
0 likes · 15 min read
NLP Challenges and Tagging Solutions in Sina Weibo Feed
DataFunTalk
DataFunTalk
Dec 2, 2019 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques

This article explains how Tencent tackles content understanding for personalized feed recommendation by combining traditional classification, keyword, and entity methods with deep learning embeddings, introducing an interest graph composed of taxonomy, concept, entity, and event layers to capture full context and infer user consumption intent.

NLPRecommendation Systemscontent understanding
0 likes · 14 min read
Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques
DataFunTalk
DataFunTalk
Nov 29, 2019 · Artificial Intelligence

Clever Classical Ideas in Natural Language Processing Tasks

The article highlights several ingenious pre‑deep‑learning techniques for NLP, including the Distributional Hypothesis, Bag‑of‑Words, Latent Semantic Analysis, Probabilistic Topic Models, BMES/BIO tagging schemes, and TextRank, explaining their principles, advantages, and historical significance in text representation and processing.

NLPbag-of-wordsdistributional hypothesis
0 likes · 5 min read
Clever Classical Ideas in Natural Language Processing Tasks
Amap Tech
Amap Tech
Nov 29, 2019 · Artificial Intelligence

Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning

The article details Amap’s upgraded map‑search query analysis, introducing a two‑stage city‑identification system, enhanced where‑what segmentation with CRF and GBDT models, a three‑stage path‑planning pipeline, and outlines future deep‑learning and knowledge‑graph enhancements for robustness and low‑frequency query handling.

Map SearchNLPcity detection
0 likes · 14 min read
Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning
Tencent Cloud Developer
Tencent Cloud Developer
Nov 26, 2019 · Backend Development

TurboSearch: Tencent AI Lab's Next-Generation Large-Scale Search System

TurboSearch is Tencent AI Lab's next-generation large-scale search system, delivering distributed massive indexing, high-performance parallel retrieval, multi-granularity and multi-modal vector indexing, private Docker deployment, integrated NLP query analysis, extensible plugins, and robust operations for massive data and diverse search scenarios.

NLPTencent AI LabTurboSearch
0 likes · 14 min read
TurboSearch: Tencent AI Lab's Next-Generation Large-Scale Search System
Ctrip Technology
Ctrip Technology
Nov 21, 2019 · Artificial Intelligence

Designing and Deploying an NLP Model for Airline Ticket Customer Service

This article describes the end‑to‑end development of a multi‑class NLP system for Ctrip airline ticket customer service, covering problem analysis, data preprocessing, sample balancing, model architecture (TextCNN and Bi‑GRU), training strategies, performance evaluation, and online customization to achieve high accuracy in intent recognition.

Bi-GRUCustomer ServiceModel deployment
0 likes · 16 min read
Designing and Deploying an NLP Model for Airline Ticket Customer Service
Amap Tech
Amap Tech
Nov 21, 2019 · Artificial Intelligence

Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission

Recent advances in map‑search text processing replace rule‑based pipelines with machine‑learning and deep‑learning models for query analysis, error correction, rewriting, and omission, using phonetic and spatial entity correction, vector‑based similarity, and CRF sequence labeling within a three‑stage architecture of analysis, recall, and ranking to deliver more precise POI results.

Error CorrectionMap SearchNLP
0 likes · 17 min read
Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission
HomeTech
HomeTech
Nov 20, 2019 · Artificial Intelligence

Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications

This article explains how query understanding (QP) transforms user search queries into structured semantic blocks and intent categories using rule‑based NLP, entity recognition, and post‑processing, and describes its taxonomy, implementation details, and practical impact on search engine results.

Knowledge GraphNLPintent recognition
0 likes · 16 min read
Query Understanding and Intent Recognition in Search: Methods, Taxonomy, and Applications
DataFunTalk
DataFunTalk
Nov 15, 2019 · Artificial Intelligence

MT-BERT: Domain‑Adapted BERT Pre‑training and Fine‑tuning for Meituan‑Dianping NLP Tasks

This article describes the development of MT‑BERT, a BERT‑based language model pre‑trained on Meituan‑Dianping business data, its distributed mixed‑precision training pipeline, domain adaptation, knowledge‑graph integration, model compression techniques, and the wide range of downstream NLP applications achieved in the platform.

BERTDomain AdaptationKnowledge Graph
0 likes · 31 min read
MT-BERT: Domain‑Adapted BERT Pre‑training and Fine‑tuning for Meituan‑Dianping NLP Tasks
Meituan Technology Team
Meituan Technology Team
Nov 14, 2019 · Artificial Intelligence

MT-BERT: Pre‑training and Fine‑tuning Practices at Meituan‑Dianping

MT‑BERT at Meituan‑Dianping combines mixed‑precision, domain‑adapted continual pre‑training, knowledge‑graph‑aware masking, and extensive compression techniques to produce fast, accurate BERT models that power fine‑grained sentiment analysis, intent classification, recommendation reasoning, and other NLP tasks across the platform.

BERTKnowledge GraphMT-BERT
0 likes · 33 min read
MT-BERT: Pre‑training and Fine‑tuning Practices at Meituan‑Dianping
360 Tech Engineering
360 Tech Engineering
Nov 13, 2019 · Artificial Intelligence

Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform

This article introduces the Huajiao platform's text anti‑spam architecture, analyzes spam categories and challenges, compares rule‑based and machine‑learning approaches, details traditional NLP methods and the TextCNN deep‑learning model, provides its TensorFlow implementation, and describes the online deployment workflow.

CNNNLPTensorFlow
0 likes · 14 min read
Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform
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
vivo Internet Technology
vivo Internet Technology
Nov 12, 2019 · Artificial Intelligence

Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin

In an interview, Gitee’s chief architect Chen Xin explains why Elasticsearch was chosen for code search, outlines how combining search with NLP can both aid semantic understanding and enrich repository queries, and shares his views on the platform’s fast‑evolving ecosystem and upcoming community meetup.

Code searchElasticsearchGitee
0 likes · 4 min read
Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin
JD Tech Talk
JD Tech Talk
Nov 5, 2019 · Artificial Intelligence

GeoBERT: A Multi‑Task Pre‑trained Language Model for Chinese Address Text

This article introduces GeoBERT, a novel pre‑training method for Chinese address strings that leverages seven jointly constrained tasks to capture spatial semantics, administrative hierarchy, and similarity relationships, enabling downstream address classification, segmentation, POI extraction, similarity comparison, and authenticity verification with reduced annotation dependence.

Chinese languageGeoBERTGeocoding
0 likes · 15 min read
GeoBERT: A Multi‑Task Pre‑trained Language Model for Chinese Address Text
JD Tech Talk
JD Tech Talk
Oct 30, 2019 · Artificial Intelligence

Solution Overview for the Scientific Paper Recommendation Matching Competition

This article presents a comprehensive solution to a competition that requires matching description paragraphs with the three most relevant papers from a 200,000‑paper corpus, detailing background, task definition, evaluation metrics, modeling strategy, and core algorithms such as SIF, InferSent, Bi‑LSTM, and BERT.

BERTCompetitionNLP
0 likes · 9 min read
Solution Overview for the Scientific Paper Recommendation Matching Competition
DataFunTalk
DataFunTalk
Oct 21, 2019 · Artificial Intelligence

Challenges and Future Directions of Chatbots: From Virtual Life to Multimodal Interaction

This talk analyzes the technical and practical challenges of current chatbots, introduces the next‑generation paradigm of virtual life, and details how AI technologies such as speech, vision, NLP, and knowledge graphs can be combined with multimodal interaction to create more intelligent and engaging conversational agents.

AIChatbotKnowledge Graph
0 likes · 25 min read
Challenges and Future Directions of Chatbots: From Virtual Life to Multimodal Interaction
Ctrip Technology
Ctrip Technology
Oct 11, 2019 · Artificial Intelligence

Intelligent Content Extraction and Generation Practices on Ctrip's Marco Polo AI Platform

This article details Ctrip's AI‑driven Marco Polo platform, describing how large‑scale NLP pipelines combine extraction, richness evaluation, semantic matching and deep‑learning generation (CopyNet, TA‑seq2seq) to produce high‑quality recommendation reasons across multiple product scenarios.

Content ExtractionNLPRecommendation Systems
0 likes · 16 min read
Intelligent Content Extraction and Generation Practices on Ctrip's Marco Polo AI Platform
DataFunTalk
DataFunTalk
Oct 9, 2019 · Artificial Intelligence

Multilingual Content Understanding in UC International Feed Recommendation

This article presents a comprehensive overview of the challenges, requirements, and technical solutions for multilingual content understanding in UC's international information‑flow recommendation system, covering structured signal construction, low‑resource NLP techniques, transfer learning, quality modeling, and image‑based signal integration.

NLPRecommendation SystemsTransfer Learning
0 likes · 14 min read
Multilingual Content Understanding in UC International Feed Recommendation
DataFunTalk
DataFunTalk
Sep 26, 2019 · Artificial Intelligence

NLP Algorithm Practices in Alibaba's Brand Advertising

This article presents a comprehensive overview of Alibaba's brand advertising business model, its technical architecture, and the practical application of NLP algorithms—specifically brand intent recognition and short‑text relevance—detailing model evolution, evaluation results, and future research directions.

Algorithm EngineeringBERTBrand Advertising
0 likes · 14 min read
NLP Algorithm Practices in Alibaba's Brand Advertising
Qunar Tech Salon
Qunar Tech Salon
Sep 12, 2019 · Artificial Intelligence

A Comprehensive Overview of Attention Mechanisms in Deep Learning

This article systematically reviews the history, core concepts, variants, and practical implementations of attention mechanisms—from early additive and multiplicative forms to self‑attention, multi‑head attention, and recent transformer‑based models—highlighting why attention has become fundamental in modern AI research.

NLPSelf-AttentionTransformer
0 likes · 16 min read
A Comprehensive Overview of Attention Mechanisms in Deep Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 11, 2019 · Artificial Intelligence

How Cognitive Concept Graphs Power Modern Search Understanding

This article explains the motivation, challenges, architecture, and algorithms behind building a large‑scale cognitive concept graph for search, detailing data construction, concept mining, fusion, confidence scoring, hierarchical structuring, validation, service algorithms, platform access, and real‑world applications such as intent recognition and entity recommendation.

Knowledge GraphNLPcognitive concept graph
0 likes · 19 min read
How Cognitive Concept Graphs Power Modern Search Understanding
Xianyu Technology
Xianyu Technology
Aug 20, 2019 · Artificial Intelligence

How Xianyu Boosted Sales with an AI-Powered Auto-Reply Chatbot

Xianyu tackled slow seller responses, inconsistent answers, and bargaining friction by building an AI-driven auto-reply system that extracts product attributes, generates knowledge bases, and uses intent recognition and response generation models, resulting in faster replies, higher conversion rates, and reduced seller workload.

AIAlibabaChatbot
0 likes · 12 min read
How Xianyu Boosted Sales with an AI-Powered Auto-Reply Chatbot
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 19, 2019 · Artificial Intelligence

How RE2 Boosts FAQ Chatbot Accuracy: A Deep Dive into Text Matching Models

This article explains the design and evaluation of RE2, a lightweight yet expressive text‑matching framework for FAQ‑style chatbots, detailing its five‑layer architecture, block‑wise residual connections, experimental results on SNLI, MultiNLI, SciTail, Quora and WikiQA datasets, and its significant performance improvements in Alibaba’s DingXiaoMi service.

FAQ chatbotNLPdeep learning
0 likes · 13 min read
How RE2 Boosts FAQ Chatbot Accuracy: A Deep Dive into Text Matching Models
Youku Technology
Youku Technology
Aug 14, 2019 · Artificial Intelligence

Technical Analysis of “Chang'an” – The Beidou Star System for Reducing Content Uncertainty and Boosting Hit Potential

The talk details how Youku’s Beidou Star AI platform deconstructs the drama “Chang’an Twelve Hours” with NLP, computer‑vision, knowledge graphs and multi‑task deep models to quantify script, character and emotion uncertainty, enabling predictive scoring that lifted the series’ daily index above one million and outlines future hybrid decision‑engine research.

AIContent AnalyticsMedia Prediction
0 likes · 12 min read
Technical Analysis of “Chang'an” – The Beidou Star System for Reducing Content Uncertainty and Boosting Hit Potential
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 9, 2019 · Artificial Intelligence

Demystifying Attention: A Beginner’s Guide to History, Types, and Applications

This article provides a comprehensive, beginner‑friendly overview of attention mechanisms—from their origins in early neural machine translation papers to modern self‑attention, multi‑head attention, and transformer variants—explaining core concepts, common variants, and why attention has become essential across NLP and vision tasks.

NLPattention
0 likes · 18 min read
Demystifying Attention: A Beginner’s Guide to History, Types, and Applications
JD Retail Technology
JD Retail Technology
Aug 8, 2019 · Artificial Intelligence

From Word Representations to Sentiment Analysis – Talk by Dr. Feng Ao

On August 6, Dr. Feng Ao presented a comprehensive overview of the evolution of word representations and sentiment analysis, illustrating the shift from traditional linguistic features to modern pretrained models such as BERT and XLNet, and sharing practical convolutional experiments relevant to industry applications.

Artificial IntelligenceNLPPretrained Models
0 likes · 4 min read
From Word Representations to Sentiment Analysis – Talk by Dr. Feng Ao
Didi Tech
Didi Tech
Aug 2, 2019 · Artificial Intelligence

How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development

At ACL 2019, Didi unveiled DELTA, an open‑source TensorFlow‑based training framework that unifies NLP and speech tasks, offers configurable pipelines, benchmark models, and seamless deployment, enabling AI developers to quickly move from research to production while leveraging Didi’s extensive open‑source ecosystem.

AI platformNLPOpen Source
0 likes · 6 min read
How Didi’s Open‑Source DELTA Platform Accelerates NLP and Speech Model Development
JD Retail Technology
JD Retail Technology
Jul 17, 2019 · Artificial Intelligence

JDATA Absolute Semantic Recognition Challenge – Competition Summary and Winners

The JDATA Absolute Semantic Recognition Challenge concluded on July 15 with eight finalist teams presenting their models, the champion "Label Team" using BERT, judges highlighting data cleaning, model fusion, and the distinction between absolute and contextual words, and a detailed ranking of all participating groups.

Algorithm CompetitionBERTJD.com
0 likes · 6 min read
JDATA Absolute Semantic Recognition Challenge – Competition Summary and Winners
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 9, 2019 · Artificial Intelligence

Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works

This article systematically reviews the evolution of attention mechanisms—from early additive and multiplicative forms to self‑attention and multi‑head variants—explaining their core three‑step framework, key differences, and why they have become essential across NLP, vision, and broader AI applications.

NLPSelf-AttentionTransformer
0 likes · 19 min read
Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works
Beike Product & Technology
Beike Product & Technology
Jun 28, 2019 · Artificial Intelligence

Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike

This article explains how Beike, China’s largest residential service platform, leverages its massive house, client, and text data to design a multi‑layered tag architecture, detailing data sources, tag construction methods—including classification, keyword, geographic, anonymous topic, and temporal tags—and their application to improve personalized house search and recommendation.

NLPclassificationreal estate
0 likes · 14 min read
Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike
DataFunTalk
DataFunTalk
Jun 23, 2019 · Artificial Intelligence

Understanding XLNet: Differences from BERT, Innovations, and Experimental Analysis

This article examines XLNet, contrasting it with BERT by detailing its novel permutation language modeling, dual‑stream attention, and larger pre‑training data, and analyzes experimental results that show XLNet’s superior performance on reading‑comprehension, GLUE, and other NLP tasks, especially for long documents.

BERTNLPPermutation Language Model
0 likes · 27 min read
Understanding XLNet: Differences from BERT, Innovations, and Experimental Analysis
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 18, 2019 · Artificial Intelligence

From Word2Vec to Quick-Thought: A Complete Guide to Modern Embeddings

This article reviews the evolution of word and sentence embeddings, covering foundational theories like vector semantics and distributional hypothesis, practical models such as Word2Vec, GloVe, fastText, Skip‑Thought, Quick‑Thought, and evaluation techniques, while offering implementation tips and real‑world use cases.

GloVeNLPWord2Vec
0 likes · 21 min read
From Word2Vec to Quick-Thought: A Complete Guide to Modern Embeddings
DataFunTalk
DataFunTalk
Jun 10, 2019 · Artificial Intelligence

BERT Applications Across NLP Domains: Progress, Challenges, and Future Directions

This article surveys the rapid proliferation of BERT-based research over the past six months, analyzing its impact on various NLP tasks such as question answering, information retrieval, dialog systems, summarization, data augmentation, classification, and sequence labeling, while also discussing the model's strengths, limitations, and future research opportunities.

BERTData AugmentationNLP
0 likes · 52 min read
BERT Applications Across NLP Domains: Progress, Challenges, and Future Directions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 5, 2019 · Artificial Intelligence

Tracing the Evolution of Language Models: From N‑grams to GPT‑2

This article reviews the historical development of natural language processing language models, covering expert rule‑based systems, statistical n‑grams, smoothing techniques, neural network models such as NNLM, RNN, word2vec, GloVe, ELMo, and the transformer‑based breakthroughs of GPT, BERT and GPT‑2, and summarizes their impact on modern NLP tasks.

BERTGPTNLP
0 likes · 25 min read
Tracing the Evolution of Language Models: From N‑grams to GPT‑2
58 Tech
58 Tech
May 31, 2019 · Artificial Intelligence

Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT

This article reviews modern deep‑learning methods for Chinese word segmentation, comparing traditional CRF‑based approaches with BiLSTM‑CRF and BERT models, describing their architectures, training procedures, experimental results, and practical considerations for deployment.

BERTBiLSTMCRF
0 likes · 17 min read
Deep Learning Approaches for Chinese Word Segmentation: BiLSTM‑CRF and BERT
DataFunTalk
DataFunTalk
May 29, 2019 · Artificial Intelligence

General‑Domain Conversational QA: Technologies, Challenges, and Alibaba UC’s Practice

This article reviews the evolution, architecture, and key technical challenges of general‑domain conversational QA systems, describing Alibaba UC’s search background, dialogue bot types, data pipelines, and advanced methods such as transfer learning, few‑shot learning, and multi‑dimensional dialogue management.

AlibabaDialogue SystemsKnowledge Graph
0 likes · 12 min read
General‑Domain Conversational QA: Technologies, Challenges, and Alibaba UC’s Practice
Alibaba Cloud Developer
Alibaba Cloud Developer
May 27, 2019 · Artificial Intelligence

From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP

This article walks through the development of deep learning for natural language processing, starting with basic neural cells and shallow networks, then exploring CNNs, RNNs, LSTMs, TextCNN, ESIM, ELMo, and culminating with the Transformer‑based BERT model, its training objectives, fine‑tuning strategies, and performance comparisons.

BERTCNNNLP
0 likes · 19 min read
From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP
Beike Product & Technology
Beike Product & Technology
May 23, 2019 · Artificial Intelligence

Practical Applications and Challenges of Machine Learning and AI at QCon Beijing 2019

At QCon Beijing 2019, four Beike technology experts presented the practical use and challenges of machine learning for user profiling, deep‑learning‑based house‑quality scoring, intelligent customer‑service systems, and AI‑driven floor‑plan generation, summarizing the architecture, data pipelines, model evolution, and future improvement directions.

AIGaNNLP
0 likes · 16 min read
Practical Applications and Challenges of Machine Learning and AI at QCon Beijing 2019
Qunar Tech Salon
Qunar Tech Salon
Apr 29, 2019 · Artificial Intelligence

Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019

The article details the Travel team's award‑winning solution for the WSDM Cup 2019 fake‑news detection task, describing data analysis, preprocessing, label‑propagation augmentation, a BERT‑based baseline, a three‑stage multi‑level model‑fusion framework, experimental results, and future directions.

BERTData AugmentationNLP
0 likes · 12 min read
Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019
Youku Technology
Youku Technology
Apr 22, 2019 · Artificial Intelligence

Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform

The talk introduces Alibaba’s Fish Brain platform, an AI‑powered decision‑support system for entertainment that combines a three‑layer data‑model, AI‑processed basic data, and application models, leveraging NLP, computer‑vision, custom embeddings, loss functions and predictive hybrid networks to analyze content, user behavior, and forecast performance.

AIBig DataEmbedding
0 likes · 12 min read
Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform
DataFunTalk
DataFunTalk
Apr 8, 2019 · Artificial Intelligence

AI Scientific Frontier Conference 2019 – Program, Speakers, and Schedule

The AI Scientific Frontier Conference 2019, co‑hosted by the Chinese Academy of Sciences AI Alliance and Beijing Institute of Technology, gathers leading researchers to present cutting‑edge talks on AI theory, deep learning, computer vision, robotics, NLP, big data, and related applications, with detailed schedules, speaker bios, venue information, and registration details provided.

AINLPdata science
0 likes · 60 min read
AI Scientific Frontier Conference 2019 – Program, Speakers, and Schedule
Hulu Beijing
Hulu Beijing
Apr 4, 2019 · Artificial Intelligence

How BERT, GPT, and ELMo Revolutionize Language Feature Representation

Natural language processing, a cornerstone of AI, relies on language models to capture linguistic features; this article reviews classic pre‑training models—ELMo, GPT, and BERT—explaining their architectures, training objectives, and how they boost downstream NLP tasks despite data‑scarcity challenges.

BERTELMoGPT
0 likes · 10 min read
How BERT, GPT, and ELMo Revolutionize Language Feature Representation
Python Crawling & Data Mining
Python Crawling & Data Mining
Apr 4, 2019 · Artificial Intelligence

Build and Visualize Three Kingdoms Character Networks with Python

This article demonstrates how to extract the character relationships from the classic novel Romance of the Three Kingdoms using Python, building entity dictionaries, constructing a co‑occurrence social network with HarvestText and NetworkX, visualizing subgraphs, ranking important figures, detecting communities, and creating an animated view of the network’s evolution over the story.

HarvestTextNLPPython
0 likes · 13 min read
Build and Visualize Three Kingdoms Character Networks with Python
DataFunTalk
DataFunTalk
Mar 15, 2019 · Artificial Intelligence

Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots

The article explores how chatbots require personalized dense knowledge graphs, dynamic temporal graphs, subjective emotion modeling, integration with external services, and multimodal media support, while also promoting a new NLP book and a related giveaway for readers.

AIChatbotDynamic Graph
0 likes · 9 min read
Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots
Meituan Technology Team
Meituan Technology Team
Mar 14, 2019 · Artificial Intelligence

Beauty Guide: How Meituan's Information Flow Content Team Uses Text Generation Technology for Creative Optimization

Meituan’s information flow content team leverages advanced text‑generation techniques—including extractive and generative models, context‑aware representations, and Seq2Seq with attention—to overcome creative constraints, improve evaluation, and boost business metrics such as click‑through rates and user engagement through optimized titles and merchant copywriting.

AIInformation FlowMeituan
0 likes · 32 min read
Beauty Guide: How Meituan's Information Flow Content Team Uses Text Generation Technology for Creative Optimization
DataFunTalk
DataFunTalk
Mar 13, 2019 · Artificial Intelligence

A Comprehensive Overview of NLP Development and Deep Learning Models

This article reviews the history of natural language processing, explains key deep‑learning models such as NNLM, Word2vec, CNN, RNN, attention mechanisms, and Transformers, and discusses their applications, future trends, and practical considerations in NLP tasks.

NLPTransformerattention
0 likes · 38 min read
A Comprehensive Overview of NLP Development and Deep Learning Models
Mafengwo Technology
Mafengwo Technology
Mar 8, 2019 · Artificial Intelligence

How Dynamic Template Matching Transforms User Review Tag Extraction

This article explains a flexible template‑matching approach that dynamically extracts concise, user‑friendly tags from online travel reviews, detailing the system architecture, key concepts, step‑by‑step implementation, and matching rules that improve recall and relevance.

AINLPTemplate Matching
0 likes · 15 min read
How Dynamic Template Matching Transforms User Review Tag Extraction
DataFunTalk
DataFunTalk
Feb 25, 2019 · Artificial Intelligence

NLP Research and Practice at Hulu: From Historical Milestones to Product Development

This article recounts a Hulu NLP research engineer's experience, reviewing key milestones such as NNLM, Word2vec, Transformer and BERT, and then contrasting academic research with product development while illustrating real-world projects like news personalization and content embedding, and describing the supporting AI platform architecture.

AIBERTHulu
0 likes · 23 min read
NLP Research and Practice at Hulu: From Historical Milestones to Product Development
Vipshop Quality Engineering
Vipshop Quality Engineering
Feb 22, 2019 · Artificial Intelligence

How Vipshop Built an AI‑Powered Sentiment Analysis System for Real‑Time Customer Feedback

Vipshop's in‑house sentiment monitoring platform integrates web‑scraped reviews, WeChat comments and internal service messages, applying lexical sentiment scoring, dictionary‑based Chinese word segmentation, TF‑IDF keyword ranking and lightweight classification to deliver real‑time insights, alerts and actionable reports for thousands of daily user comments.

Big DataNLPSentiment Analysis
0 likes · 17 min read
How Vipshop Built an AI‑Powered Sentiment Analysis System for Real‑Time Customer Feedback
Meituan Technology Team
Meituan Technology Team
Feb 21, 2019 · Artificial Intelligence

Fake News Detection with Multi‑level BERT Fusion at WSDM Cup 2019

In the WSDM Cup 2019 fake-news detection challenge, the Meituan Travel team secured second place by combining extensive data analysis, Chinese-English BERT fine-tuning, label-propagation augmentation, and a three-level fusion framework—blending, stacking, and linear regression—that lifted weighted accuracy to 0.88156.

BERTNLPWSDM Cup
0 likes · 16 min read
Fake News Detection with Multi‑level BERT Fusion at WSDM Cup 2019
Meituan Technology Team
Meituan Technology Team
Jan 25, 2019 · Artificial Intelligence

Interview with Dr. Wang Zhongyuan: AI Research, Knowledge Graphs, and Career Journey

Dr. Wang Zhongyuan, a Renmin University graduate turned AI visionary, built a world‑class NLP center and a 1.8‑billion‑entity knowledge graph at Meituan after pioneering knowledge‑graph research at Microsoft Research Asia and leading Facebook’s entity‑linking service, emphasizing solid fundamentals, data integration, and patient, continuous learning for AI impact.

AINLPcareer
0 likes · 27 min read
Interview with Dr. Wang Zhongyuan: AI Research, Knowledge Graphs, and Career Journey
Meituan Technology Team
Meituan Technology Team
Jan 25, 2019 · Artificial Intelligence

Fine-grained User Review Sentiment Classification: AI Challenger 2018 Champion's Approach

Cheng Huige’s winning AI Challenger 2018 solution treated fine‑grained Chinese review sentiment as a 20‑aspect multi‑class task, combining a high‑capacity LSTM encoder with self‑attention, word‑and‑character embeddings, simplified ELMo pre‑training, diverse tokenizations and a weighted seven‑model ensemble (including BERT), which together delivered the competition’s top F1 performance.

BERTELMoNLP
0 likes · 14 min read
Fine-grained User Review Sentiment Classification: AI Challenger 2018 Champion's Approach
58 Tech
58 Tech
Jan 22, 2019 · Artificial Intelligence

Chinese Word Segmentation: Challenges, Methods, and Practical Practices

The article explains why Chinese word segmentation is essential for NLP tasks, outlines its fundamental difficulties such as ambiguity and out‑of‑vocabulary words, reviews dictionary‑based, statistical, and CRF approaches, and shares practical experiences from 58 Search’s production system.

CRFNLPchinese segmentation
0 likes · 21 min read
Chinese Word Segmentation: Challenges, Methods, and Practical Practices
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 22, 2019 · Artificial Intelligence

How Tmall’s “Most Concerned” Feature Uses AI to Match Reviews with Consumer Questions

The article explains how Tmall’s new “Most Concerned” module leverages NLP techniques, fastText embeddings, Bi‑LSTM classifiers, and a custom clustering algorithm to filter, group, and link consumer questions with relevant product reviews, improving the shopping experience across many product categories.

AIClusteringNLP
0 likes · 9 min read
How Tmall’s “Most Concerned” Feature Uses AI to Match Reviews with Consumer Questions
Meituan Technology Team
Meituan Technology Team
Jan 10, 2019 · Artificial Intelligence

Deep Learning and Ranking Model Evolution for Hotel Search at Meituan

The talk explains how Meituan transformed its O2O hotel search by layering a multi‑stage retrieval pipeline with intent‑aware NLP, then progressively upgrading ranking—from XGBoost to MLPs, feature‑embedding networks, and finally a Wide‑Deep multi‑task model—while tackling data sparsity, diverse scenarios, and deploying the system via TensorFlow‑Serving and the in‑house MLX platform.

MeituanNLPRanking Models
0 likes · 33 min read
Deep Learning and Ranking Model Evolution for Hotel Search at Meituan
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 4, 2019 · Artificial Intelligence

NLP-based Text Opinion Extraction and Sentiment Analysis for iQIYI Video Comments

iQIYI’s NLP pipeline—combining CRF‑based segmentation, bidirectional LSTM/GRU models with attention and a CNN classifier—automatically extracts opinion targets, sentiment words and polarity from unstructured video comments, aggregates them across users to reveal collective attitudes toward actors, plot, and visual effects, and guides future work on implicit opinions and broader sentiment domains.

NLPOpinion Miningdeep learning
0 likes · 12 min read
NLP-based Text Opinion Extraction and Sentiment Analysis for iQIYI Video Comments
DataFunTalk
DataFunTalk
Dec 27, 2018 · Artificial Intelligence

Construction and Application of a Tourism Knowledge Graph

This article explains what a tourism knowledge graph is, discusses its architecture, construction methods, practical applications such as QA and recommendation, and explores future directions integrating knowledge graphs with deep learning and multi‑domain fusion.

AIKnowledge GraphNLP
0 likes · 10 min read
Construction and Application of a Tourism Knowledge Graph
DataFunTalk
DataFunTalk
Dec 24, 2018 · Artificial Intelligence

Application Scenarios and Practical Implementation of NLP in Yuewen's Content Mining Platform

This article details the business background, technical architecture, and practical deployments of natural language processing for content mining at Yuewen, covering tag construction, knowledge‑graph building, role analysis, recommendation generation, porn and plagiarism detection, and summarizing lessons learned.

Content MiningNLPtext analysis
0 likes · 15 min read
Application Scenarios and Practical Implementation of NLP in Yuewen's Content Mining Platform
Tencent Cloud Developer
Tencent Cloud Developer
Dec 21, 2018 · Artificial Intelligence

Tencent Xiaowei Conversational AI Platform: Architecture, Models, and Applications

Tencent Xiaowei is an open, easy‑to‑integrate conversational AI platform that combines NLU, dialogue management and generation, supports multi‑turn context via Memory Networks, uses bidirectional RNN and CNN‑based intent classifiers, and powers smart speakers, TVs and customer‑service bots by leveraging Tencent’s rich content ecosystem.

Conversational AIDialogue SystemsNLP
0 likes · 11 min read
Tencent Xiaowei Conversational AI Platform: Architecture, Models, and Applications
Ctrip Technology
Ctrip Technology
Dec 19, 2018 · Artificial Intelligence

Design and Implementation of an AI-Powered Medical Dialogue Assistant

This article describes the challenges and solutions encountered while developing an AI-driven medical dialogue assistant, covering data acquisition, preprocessing, model selection such as DCNN and Bi‑LSTM‑CRF, question generation, and system architecture, with insights applicable to similar healthcare chatbot projects.

AIBiLSTMDCNN
0 likes · 13 min read
Design and Implementation of an AI-Powered Medical Dialogue Assistant
DataFunTalk
DataFunTalk
Dec 11, 2018 · Artificial Intelligence

Multi-Task Learning in Natural Language Processing

An in‑depth overview of multi‑task learning for natural language processing is presented, covering deep learning foundations, challenges, various multi‑task learning paradigms (hard, soft, shared‑private, function‑level, hierarchical, and search‑based sharing), benchmark platforms, and future research directions, illustrated with numerous diagrams.

NLPdeep learning
0 likes · 15 min read
Multi-Task Learning in Natural Language Processing
Beike Product & Technology
Beike Product & Technology
Dec 6, 2018 · Artificial Intelligence

Designing and Deploying a Real‑Estate Dialogue System: Architecture, Challenges, and Practices

The talk outlines how Beike built a real‑estate conversational AI platform, covering the market need for dialogue systems, the five technical challenges, data‑driven intent and slot extraction, model choices such as FastText and Bi‑LSTM‑CRF, a three‑layer system architecture, multi‑intent handling, and future directions like 4D viewing and an internal AI dialogue platform.

BILSTM-CRFKnowledge GraphNLP
0 likes · 26 min read
Designing and Deploying a Real‑Estate Dialogue System: Architecture, Challenges, and Practices
Meituan Technology Team
Meituan Technology Team
Nov 22, 2018 · Artificial Intelligence

Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications

Meituan Brain builds a massive multi‑modal knowledge graph of billions of entities and triples across food, entertainment, and travel, using advanced extraction, validation, fusion, and reasoning techniques to empower search, recommendation, merchant tools, and fraud detection while addressing scalability and schema‑evolution challenges.

AIKnowledge GraphMeituan
0 likes · 28 min read
Meituan Brain: Large‑Scale Knowledge Graph Construction and Applications
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 2, 2018 · Artificial Intelligence

iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications

The third iQIYI Tech Salon in Beijing, held despite strong winds, showcased five expert talks on AI‑driven video library management, NLP for entertainment content, AI‑based video encoding, traffic anti‑fraud systems, and short‑video personalized recommendation, illustrating AI’s impact on content creation, quality, security, and user experience.

AINLPRecommendation Systems
0 likes · 5 min read
iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications
Meituan Technology Team
Meituan Technology Team
Nov 1, 2018 · Artificial Intelligence

Knowledge Graphs and Their Applications in Meituan's AI Platform

Meituan’s AI platform leverages a massive “Meituan Brain” knowledge graph—combining a common‑sense graph and an encyclopedia‑scale graph of billions of reviews, millions of dishes and merchant tags—integrated with deep‑learning models to enable interpretable, cross‑scenario search, recommendation, sentiment analysis and AI‑driven dining assistance.

Artificial IntelligenceNLPText Understanding
0 likes · 21 min read
Knowledge Graphs and Their Applications in Meituan's AI Platform
DataFunTalk
DataFunTalk
Nov 1, 2018 · Artificial Intelligence

Intelligent Customer Service at Meituan: NLP Techniques, System Architecture, and Real‑World Deployment

The article presents a comprehensive overview of Meituan's intelligent customer service system, detailing its evolution, the roles of QABot, TaskBot and ChatBot, the underlying NLP and knowledge‑graph technologies, model implementations such as DSSM and Seq2seq, and the impressive operational results achieved in food‑delivery and ride‑hailing services.

MeituanNLPdeep learning
0 likes · 14 min read
Intelligent Customer Service at Meituan: NLP Techniques, System Architecture, and Real‑World Deployment
DataFunTalk
DataFunTalk
Sep 21, 2018 · Artificial Intelligence

Construction of a Second‑Hand E‑commerce Knowledge Graph and Its Application in Pricing Models

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.

Knowledge GraphNLPe-commerce
0 likes · 15 min read
Construction of a Second‑Hand E‑commerce Knowledge Graph and Its Application in Pricing Models
ITPUB
ITPUB
Sep 4, 2018 · Fundamentals

Essential Python Libraries Every Data Scientist Should Know

This article surveys the most useful open‑source Python packages for data science, covering core numerical libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, while showing GitHub contribution metrics and Google‑trend popularity.

LibrariesNLPPython
0 likes · 12 min read
Essential Python Libraries Every Data Scientist Should Know