NER | BestHub

Airbnb Technology Team

Apr 15, 2024 · Artificial Intelligence

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

Airbnb’s Attribute Prioritization System uses a machine‑learning pipeline called LATEX to extract and map guest‑mentioned amenities, activities and places from reviews, messages and tickets, then predicts and ranks the most important attributes per listing, giving hosts personalized suggestions to improve listings and match traveler needs.

AirbnbNERNLP

0 likes · 9 min read

Airbnb's Attribute Prioritization System: Machine Learning for Extracting Guest Preferences from Unstructured Text

Airbnb Technology Team

Jan 31, 2024 · Artificial Intelligence

Airbnb’s Listing Attribute Extraction Platform (LAEP): End-to-End Structured Information Extraction Using Machine Learning and NLP

Airbnb’s Listing Attribute Extraction Platform (LAEP) uses a custom NER model, word‑embedding mapping, and a BERT‑based scorer to automatically pull, normalize, and validate structured attributes from hosts’ unstructured text, boosting coverage for downstream tools and enhancing guest‑host matching at scale.

AirbnbBERTNER

0 likes · 11 min read

Airbnb’s Listing Attribute Extraction Platform (LAEP): End-to-End Structured Information Extraction Using Machine Learning and NLP

AntTech

Nov 15, 2023 · Artificial Intelligence

Reading Order Matters: Information Extraction from Visually‑rich Documents by Token Path Prediction

The paper identifies reading‑order disorder as a critical obstacle in visually‑rich document information extraction, proposes a Token Path Prediction model with grid‑label formulation, introduces re‑annotated FUNSD‑r and CORD‑r datasets, and demonstrates SOTA performance on NER, entity linking, and reading‑order prediction tasks.

Document AINERlayout analysis

0 likes · 17 min read

Reading Order Matters: Information Extraction from Visually‑rich Documents by Token Path Prediction

HomeTech

Sep 8, 2022 · Artificial Intelligence

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

This article presents a comprehensive overview of concept tag mining for recommendation systems, describing unsupervised pattern‑matching, semi‑supervised AutoPhase, and supervised NER approaches, analyzing their advantages and drawbacks, and offering practical solutions to tag duplication and quality issues.

NERNLPRecommendation systems

0 likes · 11 min read

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

DataFunSummit

Mar 28, 2022 · Artificial Intelligence

Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions

This talk presents a comprehensive overview of music-domain Named Entity Recognition, covering its definition, unique challenges, candidate generation, training data construction, offline and online system architecture, successive model improvements (V1‑V3), knowledge‑fusion techniques, and future research directions.

Artificial IntelligenceKnowledge FusionMusic

0 likes · 25 min read

Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions

DataFunTalk

Mar 4, 2022 · Artificial Intelligence

Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions

This article presents a comprehensive overview of named entity recognition (NER) for the music domain, covering its definition, historical development, specific challenges such as domain relevance and ambiguity, and detailed offline and online system architectures including candidate generation, training data construction, model iterations (V1‑V3), knowledge‑fusion frameworks, and future research directions.

Artificial IntelligenceKnowledge FusionMusic

0 likes · 25 min read

58 Tech

Aug 19, 2021 · Artificial Intelligence

Practical NER Techniques for Business Chatbots on the 58.com Service Platform

This article presents a comprehensive case study of applying named‑entity‑recognition (NER) techniques to the smart chat assistant of 58.com’s yellow‑page service, covering business background, model selection (BiLSTM‑CRF, IDCNN‑CRF, BERT), data‑augmentation, focal loss, fusion of rule‑based and neural methods, context modeling, online performance, and future research directions.

BERTCRFEntity Recognition

0 likes · 16 min read

Practical NER Techniques for Business Chatbots on the 58.com Service Platform

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

Sohu Tech Products

Apr 11, 2019 · Artificial Intelligence

Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation

This article reviews the challenges of media‑domain named entity recognition, outlines the evolution from rule‑based methods through traditional machine‑learning and deep‑learning models to attention‑based Transformers, and details Sohu’s practical Bi‑LSTM‑CRF system with data‑annotation strategies and performance results.

Bi-LSTMCRFMedia

0 likes · 12 min read

Media Domain Named Entity Recognition: Techniques, Evolution, and Sohu’s Practical Implementation