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AntTech
AntTech
Mar 3, 2021 · Artificial Intelligence

Ant Group Intelligent Service Research Overview: NLP, Dialogue, Recommendation, and Anti‑fraud Papers

The article presents a comprehensive overview of Ant Group's intelligent service research, summarizing recent AI‑focused papers on text classification, stance detection, data augmentation, knowledge distillation for ranking, reinforcement‑learning‑based dialogue clarification, behavior‑cloning dialogue systems, anti‑fraud outbound bots, tag‑based service recommendation, and multi‑agent service groups, while also highlighting future directions and recruitment opportunities.

AI researchAnti‑fraudDialogue Systems
0 likes · 17 min read
Ant Group Intelligent Service Research Overview: NLP, Dialogue, Recommendation, and Anti‑fraud Papers
DataFunTalk
DataFunTalk
Feb 26, 2021 · Artificial Intelligence

Fine‑Grained Sentiment Analysis and Opinion Quadruple Extraction: Methods, Tasks, and Applications

This article introduces the concepts, tasks, and recent advances in text sentiment analysis, focusing on attribute‑level sentiment (TG‑ABSA) and opinion‑quadruple extraction, describing unsupervised, reading‑comprehension, and multi‑task deep‑learning approaches, their implementation on Huawei Cloud, experimental results, and future research directions.

Deep LearningNLPSentiment Analysis
0 likes · 20 min read
Fine‑Grained Sentiment Analysis and Opinion Quadruple Extraction: Methods, Tasks, and Applications
DataFunTalk
DataFunTalk
Feb 11, 2021 · Artificial Intelligence

How to Build Successful AI Products: Insights on AI Development, NLP, and Product Strategies

This article explores the current state of AI, the evolution of NLP and voice assistants, common pitfalls in AI product development, and practical product‑management methods—including user segmentation, metric design, and lifecycle planning—to help engineers and product managers deliver effective AI‑driven solutions.

AINLPUser experience
0 likes · 19 min read
How to Build Successful AI Products: Insights on AI Development, NLP, and Product Strategies
Efficient Ops
Efficient Ops
Feb 7, 2021 · Artificial Intelligence

How NLP Transforms Big Data Operations: Real-World AIOps Case Studies

This article explores the intersection of natural language processing and operations, outlines common text‑handling challenges, and presents three concrete AIOps case studies—log Q&A, anomaly detection, and ticket recommendation—while reflecting on a closed‑loop AI workflow and future research directions.

Big DataNLPaiops
0 likes · 9 min read
How NLP Transforms Big Data Operations: Real-World AIOps Case Studies
JD Cloud Developers
JD Cloud Developers
Feb 5, 2021 · Artificial Intelligence

2020 NLP Milestones & Future Trends: Insights from JD’s AI Scientist

In an InfoQ interview, JD Technology senior algorithm scientist Wu Youzheng reviews the rapid advances of natural language processing in 2020—including GPT‑3, multimodal dialogue, knowledge‑enhanced pre‑training, and knowledge graphs—while outlining the most promising research directions and practical challenges for the coming year.

AI applicationsGPT-3Knowledge Graph
0 likes · 18 min read
2020 NLP Milestones & Future Trends: Insights from JD’s AI Scientist
DataFunTalk
DataFunTalk
Feb 3, 2021 · Artificial Intelligence

Travel Search Technology and Innovations at Alibaba Feizhu

This article presents an in‑depth overview of Alibaba Feizhu's travel‑scene search system, covering its background, architecture, query understanding, tagging, POI mining, synonym extraction, recall strategies, model designs, performance results, and future directions for personalization and explainability.

AINLPSearch
0 likes · 18 min read
Travel Search Technology and Innovations at Alibaba Feizhu
HaoDF Tech Team
HaoDF Tech Team
Feb 2, 2021 · Artificial Intelligence

AI‑Based Structuring of Medical Examination Reports: OCR, Text Detection, Classification, and NER

This article describes how a Chinese online medical platform tackled the large‑scale extraction and structuring of hospital report images by combining OCR, deep‑learning text‑region detection, fast text classification, and advanced NER techniques, detailing challenges, algorithm choices, performance results, and remaining issues.

AINERNLP
0 likes · 19 min read
AI‑Based Structuring of Medical Examination Reports: OCR, Text Detection, Classification, and NER
New Oriental Technology
New Oriental Technology
Feb 1, 2021 · Artificial Intelligence

Neural Machine Translation: Seq2Seq, Beam Search, BLEU, Attention Mechanisms, and GNMT Improvements

This article explains key concepts of neural machine translation, covering Seq2Seq encoder‑decoder models, beam search strategies, BLEU evaluation, various attention mechanisms, and the enhancements introduced in Google's Neural Machine Translation system to improve speed, OOV handling, and translation quality.

BLEUBeam SearchGNMT
0 likes · 11 min read
Neural Machine Translation: Seq2Seq, Beam Search, BLEU, Attention Mechanisms, and GNMT Improvements
58 Tech
58 Tech
Jan 29, 2021 · Artificial Intelligence

Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service

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.

BERTCRFIDCNN
0 likes · 11 min read
Optimization Practices for Business Opportunity Slot Recognition in 58.com Intelligent Customer Service
JD Cloud Developers
JD Cloud Developers
Jan 25, 2021 · Artificial Intelligence

Top Tech Highlights: AI Advances, Open‑Source Rankings, and New Hardware in Jan 2021

This weekly roundup covers ShardingSphere's top‑5 ranking among Chinese‑led open‑source projects, Taro's entry into the 2020 China Open‑Source TOP4, Raspberry Pi Pico's $4 launch, Visual Studio's native WSL 2 support, Elasticsearch's license shift, Facebook's AI for the visually impaired, JD's future‑tech whitepaper, an AAAI paper on modal‑free segmentation, and SimCLR applied to NLP pre‑training.

AIHardwareNLP
0 likes · 6 min read
Top Tech Highlights: AI Advances, Open‑Source Rankings, and New Hardware in Jan 2021
JD Cloud Developers
JD Cloud Developers
Jan 18, 2021 · Artificial Intelligence

What’s New This Week? AI Model, MR Glasses, TypeScript 4.2, Go Generics & More

This week’s developer newsletter highlights China’s 11.3‑billion‑parameter Wenhui AI model, Rokid’s dual‑camera MR glasses, the TypeScript 4.2 beta release, a Go generics proposal for 1.18, Docker’s 2021 priorities, the Industrial Internet action plan, a robust DARTS paper from ICLR 2021, and the open‑source TextBox library for text generation.

AIDockerGo
0 likes · 8 min read
What’s New This Week? AI Model, MR Glasses, TypeScript 4.2, Go Generics & More
58 Tech
58 Tech
Jan 15, 2021 · Artificial Intelligence

Exploring Text Pre‑training Models for Dialogue Classification in Information Security: From TextCNN to RoBERTa and Knowledge Distillation

This article presents a systematic exploration of text pre‑training models for dialogue classification in information‑security scenarios, comparing baseline TextCNN, an enhanced TextCNN_role, RoBERTa with domain‑adaptive pre‑training, and a distilled mini‑model, and discusses their performance, trade‑offs, and future directions.

Dialog ModelingNLPknowledge distillation
0 likes · 13 min read
Exploring Text Pre‑training Models for Dialogue Classification in Information Security: From TextCNN to RoBERTa and Knowledge Distillation
Amap Tech
Amap Tech
Dec 30, 2020 · Artificial Intelligence

LRC-BERT: Contrastive Learning based Knowledge Distillation with COS‑NCE Loss for Efficient NLP Models

The Amap team introduced LRC‑BERT, a contrastive‑learning‑based knowledge‑distillation framework that employs a novel COS‑NCE loss, gradient‑perturbation, and a two‑stage training schedule, enabling a 4‑layer student model to retain about 97 % of BERT‑Base accuracy while being 7.5× smaller and 9.6× faster, and it has already improved real‑world traffic‑event extraction performance.

BERTCOS-NCE lossNLP
0 likes · 16 min read
LRC-BERT: Contrastive Learning based Knowledge Distillation with COS‑NCE Loss for Efficient NLP Models
DataFunSummit
DataFunSummit
Dec 27, 2020 · Artificial Intelligence

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

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.

BERTCRFHMM
0 likes · 27 min read
Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation
DataFunTalk
DataFunTalk
Dec 25, 2020 · Artificial Intelligence

Exploring Pretraining Model Optimization and Deployment Challenges in NLP

This article reviews the evolution of pretraining models in NLP, discusses the practical challenges of deploying large models such as inference latency, knowledge integration, and task adaptation, and presents Xiaomi’s optimization techniques including knowledge distillation, low‑precision inference, operator fusion, and multi‑granularity segmentation for dialogue systems.

BERTDialogue SystemsInference Optimization
0 likes · 15 min read
Exploring Pretraining Model Optimization and Deployment Challenges in NLP
58 Tech
58 Tech
Dec 23, 2020 · Artificial Intelligence

Application Practice of Intelligent Writing Technology in the 58 Community Content Platform

The article reviews the rise of AI‑driven intelligent writing robots, outlines industry use cases, explains two main generation methods, and details how 58.com leverages massive structured data to automatically produce narrative, emotion‑rich listings for vehicles, housing, and jobs, enhancing user engagement.

58.comAIContent Generation
0 likes · 6 min read
Application Practice of Intelligent Writing Technology in the 58 Community Content Platform
DataFunTalk
DataFunTalk
Dec 21, 2020 · Artificial Intelligence

Intelligent Question Answering Technology Framework and Practices at Meituan

This article describes Meituan's intelligent question answering system, detailing its three core capabilities—Document QA, Community QA, and Knowledge‑Graph QA—along with the underlying machine‑reading comprehension models, multi‑task learning, answer ranking, and real‑world deployment scenarios across travel, hotel, and retail services.

Knowledge GraphMeituanNLP
0 likes · 22 min read
Intelligent Question Answering Technology Framework and Practices at Meituan
DataFunTalk
DataFunTalk
Dec 20, 2020 · Artificial Intelligence

Complex Semantic Expression Methods in Voice Assistants: NLP Layers, DIS Limitations, and the CMRL Schema

This article explains how voice assistants rely on NLP's three processing layers, examines the shortcomings of the traditional DIS semantic structure, introduces the hierarchical CMRL schema with its six element types, and presents two neural models—copy‑write seq2seq and seq2tree—for accurate semantic parsing of complex commands.

AICMRLNLP
0 likes · 15 min read
Complex Semantic Expression Methods in Voice Assistants: NLP Layers, DIS Limitations, and the CMRL Schema
DataFunSummit
DataFunSummit
Dec 18, 2020 · Artificial Intelligence

Complex Semantic Representation in Voice Assistants: NLP Layers, DIS Limitations, and the CMRL Schema

This article explains how voice assistants rely on a three‑layer NLP pipeline (lexical, syntactic, and semantic analysis), discusses the shortcomings of the traditional DIS (Domain‑Intent‑Slot) structure for complex commands, and introduces the hierarchical CMRL schema along with two neural models (copy‑write seq2seq and seq2tree) for converting natural language into structured logical expressions.

CMRLNLPSeq2Seq
0 likes · 14 min read
Complex Semantic Representation in Voice Assistants: NLP Layers, DIS Limitations, and the CMRL Schema
DataFunSummit
DataFunSummit
Dec 14, 2020 · Artificial Intelligence

LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models

This article introduces LightSeq, an open‑source, GPU‑accelerated inference engine that dramatically speeds up Transformer‑based models such as BERT and GPT by up to 14× over TensorFlow, supports multiple decoding strategies, integrates seamlessly with major deep‑learning frameworks, and provides detailed performance benchmarks and technical optimizations.

Deep LearningGPUInference
0 likes · 15 min read
LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models
DataFunTalk
DataFunTalk
Dec 14, 2020 · Artificial Intelligence

Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions

This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.

Generative ModelsNLPinformation retrieval
0 likes · 9 min read
Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions
Python Crawling & Data Mining
Python Crawling & Data Mining
Nov 23, 2020 · Artificial Intelligence

How to Scrape Douban Reviews and Uncover Hidden Sentiment Trends with Python

This article demonstrates how to crawl Douban short reviews for the TV show "Actors Please Take Your Place" Season 2, clean and deduplicate the data, apply Baidu's SKEP sentiment model, and visualize word clouds, rating distributions, posting times, and sentiment scores, providing full Python code for replication.

DoubanNLPdata-visualization
0 likes · 10 min read
How to Scrape Douban Reviews and Uncover Hidden Sentiment Trends with Python
58 Tech
58 Tech
Nov 13, 2020 · Artificial Intelligence

Slot Recognition and Correction in Voice Robots: Methods, Models, and Experimental Results

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.

AIError CorrectionNLP
0 likes · 10 min read
Slot Recognition and Correction in Voice Robots: Methods, Models, and Experimental Results
Sohu Tech Products
Sohu Tech Products
Nov 11, 2020 · Artificial Intelligence

Illustrated Transformer: Comprehensive Explanation and Code Implementation

This article provides a step‑by‑step illustrated guide to the Transformer architecture, covering its macro structure, detailed self‑attention mechanisms, multi‑head attention, positional encoding, residual connections, decoder operation, training process, loss functions, and includes complete PyTorch and custom Python code examples.

NLPPyTorchSelf-Attention
0 likes · 33 min read
Illustrated Transformer: Comprehensive Explanation and Code Implementation
FunTester
FunTester
Nov 11, 2020 · Artificial Intelligence

Unlocking NLP: From the Turing Test to Word Embeddings and Beyond

This article provides a comprehensive overview of natural language processing, tracing its origins from Turing's seminal test to modern techniques like regular expressions, word order importance, word embeddings, Word2vec, GloVe, and knowledge‑ and retrieval‑based chatbot methods.

GloVeKnowledge GraphsNLP
0 likes · 15 min read
Unlocking NLP: From the Turing Test to Word Embeddings and Beyond
Sohu Tech Products
Sohu Tech Products
Nov 4, 2020 · Artificial Intelligence

Understanding BERT: Architecture, Pre‑training, Fine‑tuning and Applications in Modern NLP

This article provides a comprehensive overview of BERT and related NLP advances, covering its historical context, model architecture, input‑output mechanisms, comparisons with CNNs, word‑embedding evolution, pre‑training strategies like MLM and next‑sentence prediction, and practical guidance for fine‑tuning and feature extraction.

BERTFine-tuningNLP
0 likes · 17 min read
Understanding BERT: Architecture, Pre‑training, Fine‑tuning and Applications in Modern NLP
JD Cloud Developers
JD Cloud Developers
Nov 4, 2020 · Artificial Intelligence

Multimodal AI Breakthroughs Unveiled at NLPCC 2020 Workshop

The article recaps the inaugural Multimodal Natural Language Processing workshop at NLPCC 2020, highlighting breakthroughs in multimodal summarization, pre‑training models, AI‑driven art, visual‑language interaction, and multimodal dialogue systems, and showcases research from leading institutions and industry partners.

AIMultimodalNLP
0 likes · 9 min read
Multimodal AI Breakthroughs Unveiled at NLPCC 2020 Workshop
58 Tech
58 Tech
Oct 9, 2020 · Artificial Intelligence

Speaker Role Recognition in an Intelligent Voice Analysis Platform

This article describes a speaker role recognition system for a voice analysis platform, detailing a gender‑based pre‑filter, keyword‑matching and TextCNN‑based text classification, and single‑sentence correction methods that together improve role assignment accuracy by about 6% over baseline third‑party solutions.

AINLPTextCNN
0 likes · 12 min read
Speaker Role Recognition in an Intelligent Voice Analysis Platform
AntTech
AntTech
Sep 27, 2020 · Artificial Intelligence

Question Directed Graph Attention Network for Numerical Reasoning over Text (QDGAT)

The paper introduces QDGAT, a question‑directed graph attention network that enhances numerical reasoning in reading comprehension by explicitly modeling relationships among numbers, entities, and questions, and demonstrates its effectiveness through extensive experiments on the DROP dataset.

DROPKnowledge GraphNLP
0 likes · 7 min read
Question Directed Graph Attention Network for Numerical Reasoning over Text (QDGAT)
Tencent Cloud Developer
Tencent Cloud Developer
Sep 23, 2020 · Artificial Intelligence

NLP Model Interpretability: White-box and Black-box Methods and Business Applications

The article reviews NLP interpretability techniques, contrasting white‑box approaches that probe model internals such as neuron analysis, diagnostic classifiers, and attention with black‑box strategies like rationales, adversarial testing, and local surrogates, and argues that black‑box methods are generally more practical for business deployment despite offering shallower insights.

Attention MechanismBERTDeep Learning
0 likes · 12 min read
NLP Model Interpretability: White-box and Black-box Methods and Business Applications
DataFunTalk
DataFunTalk
Sep 23, 2020 · Artificial Intelligence

From Word Embedding to BERT: A Comprehensive Overview of Pre‑training Model Development in NLP

This article surveys the evolution of pre‑training models for natural language processing, detailing model architectures such as Encoder‑AE, Decoder‑AR, Encoder‑Decoder, Prefix LM, and PLM, analyzing why models like RoBERTa, T5, and GPT‑3 excel, and offering practical guidance for building strong pre‑training systems.

BERTNLPTransformer
0 likes · 47 min read
From Word Embedding to BERT: A Comprehensive Overview of Pre‑training Model Development in NLP
DataFunTalk
DataFunTalk
Sep 19, 2020 · Artificial Intelligence

AliCoCo: Alibaba’s E‑commerce Cognitive Concept Net – Architecture, Construction, and Applications

The article presents AliCoCo, Alibaba’s large‑scale e‑commerce knowledge graph that models user demand as concepts, describes its four‑layer architecture, the algorithms for concept extraction, taxonomy building, and item association, and demonstrates its impact on search and recommendation systems.

AlibabaKnowledge GraphNLP
0 likes · 22 min read
AliCoCo: Alibaba’s E‑commerce Cognitive Concept Net – Architecture, Construction, and Applications
DataFunTalk
DataFunTalk
Sep 16, 2020 · Artificial Intelligence

Hotspot Mining and Event Extraction in Tencent Information Flow: Methods, Framework, and Applications

This article presents Tencent's research on hotspot mining and event extraction for information flow, detailing the challenges of timeliness, comprehensiveness, and heat rationality, the combined use of time‑series analysis, topic detection, clustering, and dynamic‑time‑warping, and the resulting framework and its applications to text, image, and video recommendation.

Event ExtractionNLPTime Series Analysis
0 likes · 17 min read
Hotspot Mining and Event Extraction in Tencent Information Flow: Methods, Framework, and Applications
MaGe Linux Operations
MaGe Linux Operations
Sep 13, 2020 · Artificial Intelligence

Beyond Pandas: 10 Lesser‑Known Python Libraries Every Data Scientist Should Try

This article introduces a curated collection of lesser‑known Python libraries for data‑science tasks—including wget, pendulum, imbalanced‑learn, flashtext, fuzzywuzzy, pyflux, ipyvolume, dash, and gym—detailing their purpose, installation commands, and concise code examples to help practitioners expand their toolkit.

NLPPythonTime Series
0 likes · 9 min read
Beyond Pandas: 10 Lesser‑Known Python Libraries Every Data Scientist Should Try
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 28, 2020 · Artificial Intelligence

Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide

This comprehensive guide examines real‑world text classification projects, covering label taxonomy design, data scarcity solutions, efficient annotation, new‑class discovery, algorithm selection, evaluation metrics, OOV handling, model evolution, rule‑model integration, performance‑boosting tricks, and inference under resource constraints.

Few‑Shot LearningModel EvaluationNLP
0 likes · 15 min read
Avoid Common Pitfalls in Industrial Text Classification: A Practical Guide
Didi Tech
Didi Tech
Aug 23, 2020 · Artificial Intelligence

DiDi AI Labs Achieves Third Place in WMT2020 News Translation Task

DiDi AI Labs’ NLP team earned third place in the WMT2020 Chinese‑to‑English news translation task with a 36.6 BLEU score, using an enhanced Transformer‑2 model that incorporates self‑attention, relative positional attention, iterative back‑translation, knowledge distillation, data cleaning, ensembling, and other techniques, now deployed across DiDi’s international services.

BLEUDiDi AI LabsNLP
0 likes · 5 min read
DiDi AI Labs Achieves Third Place in WMT2020 News Translation Task
MaGe Linux Operations
MaGe Linux Operations
Aug 20, 2020 · Artificial Intelligence

Explore 10 Lesser-Known Python Libraries for Data Science & AI

This article introduces a curated selection of lesser‑known Python packages—such as wget, pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, ipyvolume, Dash, and Gym—detailing their installation commands, core functionalities, and code examples to help data scientists expand their toolkit beyond the usual pandas, scikit‑learn, and matplotlib.

Data ScienceNLPPython
0 likes · 9 min read
Explore 10 Lesser-Known Python Libraries for Data Science & AI
Sohu Tech Products
Sohu Tech Products
Aug 19, 2020 · Artificial Intelligence

ASR Error Correction with BERT, ELECTRA and a Fuzzy‑Phoneme Generator: Techniques from Xiaomi AI

This article describes how Xiaomi's AI team tackles Automatic Speech Recognition (ASR) query errors by analyzing error patterns, employing BERT, ELECTRA and a soft‑masked BERT model, generating synthetic noisy data with a fuzzy‑phoneme generator, and presenting experimental results and future research directions.

ASRBERTDeep Learning
0 likes · 18 min read
ASR Error Correction with BERT, ELECTRA and a Fuzzy‑Phoneme Generator: Techniques from Xiaomi AI
58 Tech
58 Tech
Aug 12, 2020 · Artificial Intelligence

Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition

This article provides a step‑by‑step tutorial on preparing data, pre‑training the SPTM language model, fine‑tuning a text‑classification model, generating predictions, and creating a submission file for the 58.com AI algorithm competition using the open‑source qa_match toolkit.

AIModel TrainingNLP
0 likes · 9 min read
Guide to Using SPTM (Simple Pre-trained Model) with qa_match for an AI Competition
MaGe Linux Operations
MaGe Linux Operations
Aug 6, 2020 · Artificial Intelligence

Build a Python Chatbot with ChatterBot: Step‑by‑Step Guide

This article introduces chatbots, explains rule‑based and self‑learning types, describes how AI‑driven bots work, and provides a complete Python tutorial using the ChatterBot library—including environment setup, installation, and full source code for a functional chatbot.

ChatbotChatterBotNLP
0 likes · 4 min read
Build a Python Chatbot with ChatterBot: Step‑by‑Step Guide
DataFunTalk
DataFunTalk
Aug 3, 2020 · Artificial Intelligence

Advances in Sequence‑to‑Sequence Text Generation: Attention, Pointer, Copy, and Transformer Models

This article reviews the evolution of encoder‑decoder based text generation, covering classic seq2seq with attention, pointer networks, copy mechanisms, knowledge‑enhanced models, convolutional approaches, and the latest Transformer‑based pre‑training such as MASS, highlighting their architectures, key innovations, and practical considerations.

NLPSeq2SeqText Generation
0 likes · 17 min read
Advances in Sequence‑to‑Sequence Text Generation: Attention, Pointer, Copy, and Transformer Models
58 Tech
58 Tech
Aug 3, 2020 · Artificial Intelligence

Intelligent Customer Service Competition: Leveraging AI for Text Matching and Classification

This announcement describes the rise of AI‑driven intelligent customer service, highlights 58.com’s long‑standing system, and introduces a competition that provides real‑world data for participants to develop advanced text‑matching and classification models using state‑of‑the‑art NLP techniques.

Artificial IntelligenceDatasetNLP
0 likes · 3 min read
Intelligent Customer Service Competition: Leveraging AI for Text Matching and Classification
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 24, 2020 · Artificial Intelligence

Fine‑grained Character Sentiment Analysis in Scripts: Models, Challenges, and Future Directions

The article surveys fine‑grained character sentiment analysis for script evaluation, detailing traditional, target‑dependent and aspect‑level methods, describing iQIYI’s BERT‑TD‑LSTM and CNN architectures, addressing challenges such as character name recognition and long‑range context, and outlining future improvements after a Parasite case study.

BERTLSTMNLP
0 likes · 19 min read
Fine‑grained Character Sentiment Analysis in Scripts: Models, Challenges, and Future Directions
58 Tech
58 Tech
Jul 22, 2020 · Artificial Intelligence

Intelligent Customer Service Competition: Leveraging AI for Text Matching and Classification

The announcement introduces an AI‑driven intelligent customer service competition, highlighting the importance of text matching and classification in NLP, describing 58.com’s existing system, providing a real‑world dataset, and inviting participants to develop precise models using the latest deep‑learning techniques.

AIDatasetNLP
0 likes · 4 min read
Intelligent Customer Service Competition: Leveraging AI for Text Matching and Classification
DataFunTalk
DataFunTalk
Jul 15, 2020 · Artificial Intelligence

ASR Error Correction with BERT, ELECTRA, and a Fuzzy‑Phoneme Generator: Methods, Experiments, and Future Directions

This article presents a comprehensive overview of automatic speech recognition (ASR) error correction techniques employed by Xiaomi's Xiao‑Ai team, detailing problem definition, related work on BERT and ELECTRA, a custom generator‑discriminator architecture with a fuzzy‑phoneme simulator, experimental results, and prospective research directions.

ASRBERTELECTRA
0 likes · 19 min read
ASR Error Correction with BERT, ELECTRA, and a Fuzzy‑Phoneme Generator: Methods, Experiments, and Future Directions
58 Tech
58 Tech
Jul 10, 2020 · Artificial Intelligence

Tag Mining for Used‑Car Business: NLP, Word2Vec, and Retrieval Pipeline

This article details the end‑to‑end process of extracting and leveraging tags for used‑car listings, covering data collection, segmentation, NLP‑based tokenization, word‑vector generation, tag‑library construction, and online retrieval flow to improve personalized recall and CTR.

NLPTaggingWord2Vec
0 likes · 19 min read
Tag Mining for Used‑Car Business: NLP, Word2Vec, and Retrieval Pipeline
DataFunTalk
DataFunTalk
Jul 7, 2020 · Artificial Intelligence

Optimizing Pretrained Language Model Inference: Lessons from the NLPCC Small Model Competition and Deployment at Xiaomi

This article shares the Xiaomi AI Lab NLP team's experience in the NLPCC lightweight language model competition, discusses efficiency challenges of large pretrained models like BERT, and details practical inference optimizations—including model distillation, batching, FP16 quantization, and FasterTransformer integration—that dramatically reduce latency and hardware costs in production.

AIBERTInference Optimization
0 likes · 15 min read
Optimizing Pretrained Language Model Inference: Lessons from the NLPCC Small Model Competition and Deployment at Xiaomi
Tencent Advertising Technology
Tencent Advertising Technology
Jun 22, 2020 · Artificial Intelligence

Graph-based Evidence Aggregating and Reasoning (GEAR) Model for Fact Verification in NLP

The article explains how the GEAR model uses graph neural networks and BERT representations to aggregate multiple pieces of evidence for fact verification, improving accuracy on datasets like FEVER and offering applications in misinformation detection, knowledge‑graph completion, and advertising analytics.

BERTGEAR modelNLP
0 likes · 8 min read
Graph-based Evidence Aggregating and Reasoning (GEAR) Model for Fact Verification in NLP
DataFunTalk
DataFunTalk
Jun 12, 2020 · Artificial Intelligence

Content Understanding for Advertising on Weibo: Challenges, Solutions, and Applications

This article explains how Weibo's advertising platform leverages content understanding—covering system architecture, problems caused by insufficient comprehension, the construction of NLP and vision capabilities, content‑based ad strategies, and a celebrity‑brand knowledge graph—to improve ad relevance and ROI.

AdvertisingKnowledge GraphNLP
0 likes · 13 min read
Content Understanding for Advertising on Weibo: Challenges, Solutions, and Applications
NetEase Media Technology Team
NetEase Media Technology Team
Jun 12, 2020 · Artificial Intelligence

Semantic Text Understanding for NetEase News Feed Recommendation

NetEase improves its news‑feed recommendation by applying a multi‑stage semantic text understanding pipeline—lexical analysis, hierarchical content tagging, and quality filtering—using two‑level classifiers, LDA‑based topic modeling, multi‑label concept and entity extraction, and dense vector representations to better capture user interests and boost personalization performance.

NLPRecommendation Systemsfeature engineering
0 likes · 9 min read
Semantic Text Understanding for NetEase News Feed Recommendation
DataFunTalk
DataFunTalk
Jun 8, 2020 · Artificial Intelligence

Recap of Baidu ACL 2020 Paper Sharing Session – Papers 4 to 6

The Baidu ACL 2020 paper sharing live session recap presents three NLP research papers—on unsupervised style transfer, sentiment‑knowledge‑enhanced pre‑training, and conversational recommendation over multi‑type dialogs—detailing their novel models, methodologies, and key contributions.

ACL 2020NLPSentiment Analysis
0 likes · 4 min read
Recap of Baidu ACL 2020 Paper Sharing Session – Papers 4 to 6
DataFunTalk
DataFunTalk
May 25, 2020 · Artificial Intelligence

NLP Techniques for Financial Investment Analysis: Case Studies from Two Sigma, BlackRock, UC Berkeley and Others

This article reviews how natural language processing is used in financial investment analysis, summarizing case studies from Two Sigma, BlackRock, UC Berkeley and other institutions that apply topic modeling, event extraction and sentiment analysis to improve portfolio performance and achieve excess returns.

Event ExtractionInvestment AnalysisNLP
0 likes · 17 min read
NLP Techniques for Financial Investment Analysis: Case Studies from Two Sigma, BlackRock, UC Berkeley and Others
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
May 25, 2020 · Artificial Intelligence

How AI Turns Dark Data into Actionable Automation

This article explains how enterprises can classify structured, semi‑structured and unstructured data as “dark data”, why traditional RPA struggles with it, and how AI technologies like NLP, computer vision and machine learning—exemplified by Automation Anywhere’s IQ‑Bot—enable end‑to‑end automation of hidden information.

AIIQ BotNLP
0 likes · 9 min read
How AI Turns Dark Data into Actionable Automation
Meituan Technology Team
Meituan Technology Team
May 21, 2020 · Artificial Intelligence

AIS 2020 Conference: Schedule and Speakers for Top NLP/AI/IR Papers

The AIS 2020 Conference, co‑hosted by the Beijing Academy of Artificial Intelligence and Meituan, showcased 74 top ACL, IJCAI and SIGIR papers across 15 sessions on NLP, AI and IR topics, streamed free online on May 23‑24 2020 with keynote speakers from leading Chinese universities.

AINLPconference
0 likes · 12 min read
AIS 2020 Conference: Schedule and Speakers for Top NLP/AI/IR Papers
DataFunTalk
DataFunTalk
May 18, 2020 · Artificial Intelligence

Intelligent Investment Research and Financial Sentiment Monitoring with NLP and Big Data

This article describes how advanced natural‑language‑processing, big‑data, and deep‑learning techniques are integrated into an end‑to‑end platform for financial asset management, covering large‑scale bid‑tender text analysis, few‑shot sentiment monitoring, model architectures, data‑enhancement methods, and practical deployment results.

Big DataFew‑Shot LearningFinancial AI
0 likes · 28 min read
Intelligent Investment Research and Financial Sentiment Monitoring with NLP and Big Data
DataFunTalk
DataFunTalk
May 17, 2020 · Artificial Intelligence

Improving Text Representation and Clustering for Small‑Sample Scenarios in 58 Second‑Hand Car Intelligent Customer Service

This article presents a study on enhancing text representation and clustering in a small‑sample setting for 58's second‑hand car intelligent customer service by introducing a Bi‑LSTM based pre‑training language model and an improved Deep Embedded Clustering (DEC) algorithm, demonstrating significant gains in accuracy, silhouette score, and answer‑rate through extensive experiments.

AIBi-LSTMDEC
0 likes · 16 min read
Improving Text Representation and Clustering for Small‑Sample Scenarios in 58 Second‑Hand Car Intelligent Customer Service
Qunar Tech Salon
Qunar Tech Salon
May 13, 2020 · Artificial Intelligence

Intelligent Hotel Post‑Sale QA System: Model Selection, Evaluation, and Engineering Optimization

This article describes the design, model selection, experimental evaluation, and engineering optimization of an AI‑driven post‑sale question‑answering system for hotel services, covering FAQ construction, intent detection, deep‑learning matching models such as DSSM, ESIM, BERT, and their performance and latency trade‑offs.

AIBERTDSSM
0 likes · 14 min read
Intelligent Hotel Post‑Sale QA System: Model Selection, Evaluation, and Engineering Optimization
iQIYI Technical Product Team
iQIYI Technical Product Team
May 8, 2020 · Artificial Intelligence

Introduction to NLP in Video Applications

When you browse video apps by tags, receive personalized recommendations, or search with keywords, the seamless experience is powered by Natural Language Processing, which analyzes and interprets textual data to connect users with relevant content, and the article invites you to scan a QR code for further exploration.

AI applicationsArtificial IntelligenceNLP
0 likes · 9 min read
Introduction to NLP in Video Applications
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.

NLPQuery Understandinginformation retrieval
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.

AIKnowledge BaseNLP
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.

Deep LearningNLPentity 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.

NLPdata augmentationmachine 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.

BERTLanguage ModelNLP
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.

AICtripImage Classification
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.

NLPcity detectionmachine learning
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-GRUDeep LearningModel 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 CorrectionNLPQuery Rewriting
0 likes · 17 min read
Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission