arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.
arXiv:2610.09063v1 Announce Type: new Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability c...
The paper "InsurTech innovation using natural language processing" outlines how insurance firms are adopting NLP to convert unstructured text into structured data for actuarial analysis. It presents case studies that use alternative data from an InsurTech partner to demonstrate feature de‑biasing, compression, and new industry classification methods in commercial insurance. These techniques enhance traditional rating factors and provide fresh risk assessment perspectives, positioning NLP as a core component of contemporary insurance analytics.
arXiv:2607. 03880v1 Announce Type: cross Abstract: Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries.
arXiv:2607. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.
arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).
arXiv:2607. 06818v1 Announce Type: cross Abstract: For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers.
arXiv:2606. 26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement.
The paper presents a hybrid generative-tagging decoding approach for extracting product attribute-value pairs in e-commerce. It combines a tagging head that directly copies frequent attribute values from the source text with a generative head that handles rare attributes and non-copyable values. Experiments integrated with vLLM inference show a 10% increase in attribute coverage, a 63% rise in correct extractions, and a 44% reduction in latency without sacrificing precision.
The paper presents the CDSP (context-conditional deliberation signal pipeline), which transforms investment committee meeting transcripts into structured predictive features. CDSP segments transcripts into topical chunks, assigns asset‑class context labels via a large language model, maps financial keywords to a taxonomy, and adds sentiment polarity and mention frequency features. Using these engineered features on 48 monthly meetings, the best model—combining sentence embeddings with CDSP features—achieves 73% accuracy and a 0.73 F1 score, outperforming a simple stock‑choice baseline, though the improvement is not statistically significant.
arXiv:2606. 24655v1 Announce Type: cross Abstract: The explosive growth and complexity of product data within the dynamic Brazilian e-commerce landscape demand robust and specialized methods for structured information extraction.
arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.