Suchergebnisse - SQLNet*

SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting

Autoren: Hefeng Wu Yandong Chen Lingbo Liu et al.

Quelle: IEEE Transactions on Image Processing. 34:4631-4645

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Vision and Pattern Recognition

Zugangs-URL: http://arxiv.org/abs/2311.10011

Text to SQL Transformation Using LLM: A Comparative Research of T5, Seq2Seq, and SQLNet Models

Autoren: Zhazira Shaikhiyeva Madina Mansurova Gulshat Amirkhanova

Quelle: 2024 9th International Conference on Computer Science and Engineering (UBMK). :938-943

SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting

Autoren: Wu, Hefeng Chen, Yandong Liu, Lingbo et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2311.10011

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Autoren: Xu, Xiaojun Liu, Chang Song, Dawn

Index Begriffe: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Databases, text

URL: http://arxiv.org/abs/1711.04436

Automated feature generation for machine learning application

TUR2SQL: A Cross-Domain Turkish Dataset For Text-to-SQL

Autoren: Kanburoğlu, Ali Buğra Tek, Faik Boray

Quelle: 2023 8th International Conference on Computer Science and Engineering (UBMK). :206-211

Schlagwörter: Text-to-SQL, 0301 basic medicine, Performance, Deep learning, SQLNet, Natural languages, 02 engineering and technology, Cross-domain, SQL query, Turkish language, Natural language processing systems, 03 medical and health sciences, ChatGPT, Turkishs, 0202 electrical engineering, electronic engineering, information engineering, Dataset

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11729/5803

SICPAT - SQLNet8

Autoren: Oracle

Schlagwörter: SQLNET, Instalador SQLNET, Centro Nacional de Cálculo, Cenac, IPN, Instituto Politécnico Nacional, SICPAT, Sistema Institucional de Control Patrimonial, Ejecutable, Zip

Relation: http://www.repositoriodigital.ipn.mx/handle/123456789/8833

Verfügbarkeit: http://www.repositoriodigital.ipn.mx/handle/123456789/8833

An SQL query generator for cross-domain human language based questions based on NLP model.

Autoren: Naik, B. Balaji Reddy, T. Jaya Venkata Rama karthik, K. Rohith Venkata et al.

Quelle: Multimedia Tools & Applications; Jan2024, Vol. 83 Issue 4, p11861-11884, 24p

Schlagwörter: SQL, HUMAN beings

GEMMA-SQL: A Novel Text-to-SQL Model Based on Large Language Models.

Autoren: Pandey, Hari Mohan Gupta, Anshul Sarkar, Subham et al.

Quelle: Applied Artificial Intelligence; Dec2025, Vol. 39 Issue 1, p1-38, 38p

Schlagwörter: LANGUAGE models, SQL, MACHINE learning, NATURAL language processing, OPEN source software

A Spatial Attention-Based Transductive Long Short-Term Memory for Semantic Parsing Trained with a Semi-Supervised Method.

Autoren: Chen, Shichao

Quelle: Journal of the Institution of Engineers (India): Series B; Dec2025, Vol. 106 Issue 6, p1885-1900, 16p

Learning database optimization techniques: the state-of-the-art and prospects.

Autoren: Qiao, Shao-Jie Fan, Han-Lin Han, Nan et al.

Quelle: Frontiers of Computer Science; Dec2025, Vol. 19 Issue 12, p1-24, 24p

A comprehensive review of LLM applications for lung cancer diagnosis and treatment: classification, challenges, and future directions.

Autoren: Ghorbian, Mohsen Ghobaei-Arani, Mostafa

Quelle: Journal of Big Data; 11/11/2025, Vol. 12 Issue 1, p1-45, 45p

Schlagwörter: LUNG cancer, LANGUAGE models, DISEASE complications, DIAGNOSIS, GENETICS, COMPUTER-assisted image analysis (Medicine), ARTIFICIAL intelligence, THERAPEUTICS

DocSpider: a dataset of cross-domain natural language querying for MongoDB.

Autoren: Özer, Arif Görkem Cekinel, Recep Firat Toroslu, Ismail Hakki et al.

Quelle: Natural Language Processing (29770424); Nov2025, Vol. 31 Issue 6, p1367-1398, 32p

Schlagwörter: LANGUAGE models, NONRELATIONAL databases, DATABASES, NATURAL languages, BIG data

DKASQL: Dynamic Knowledge Adaptation for Domain-Specific Text-to-SQL.

Autoren: Bian, Huaxing Li, Guanrong Wang, Yifan et al.

Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 20, p11121, 21p

Schlagwörter: DOMAIN specificity, SQL, COMPUTER performance, LANGUAGE models

Robust Clinical Querying with Local LLMs: Lexical Challenges in NL2SQL and Retrieval-Augmented QA on EHRs.

Autoren: Blašković, Luka Tanković, Nikola Lorencin, Ivan et al.

Quelle: Big Data & Cognitive Computing; Oct2025, Vol. 9 Issue 10, p256, 40p

Schlagwörter: LANGUAGE models, MEDICAL records, DATA security, COMPUTATIONAL linguistics, PERIODICAL articles, NATURAL language processing

A Survey on Employing Large Language Models for Text-to-SQL Tasks.

Autoren: Shi, Liang Tang, Zhengju Zhang, Nan et al.

Quelle: ACM Computing Surveys; Feb2026, Vol. 58 Issue 2, p1-37, 37p

Schlagwörter: LANGUAGE models, NATURAL language processing, EVALUATION methodology

多维上下文关系感知的SQL 自动生成方法. (Chinese)

Alternate Title: SQL automatic generation method with multi-dimensional context-relation awareness. (English)

Autoren: 刘晨旭 王邦平 宋海权 et al.

Quelle: Journal of Chongqing University of Technology (Natural Science); 2025, Vol. 39 Issue 9, p124-132, 9p

Schlagwörter: NATURAL language processing, CONTEXT-aware computing, MACHINE learning, APPLIED sciences

Enhancing database query interpretation: a comparative analysis of semantic parsing models.

Autoren: Keswani, Gunjan Chandak, Manoj B.

Quelle: International Journal of Informatics & Communication Technology (IJ-ICT); Aug2025, Vol. 14 Issue 2, p467-477, 11p

Schlagwörter: PROCESS capability, INFORMATION retrieval, DATABASES, ELECTRONIC data processing, DATABASE design

Experience Report: Hey LLM, Generate SQL!

Autoren: Heinz, Florian Schildgen, Johannes

Quelle: Datenbank-Spektrum; Jul2025, Vol. 25 Issue 2, p95-101, 7p

Schlagwörter: SQL, LANGUAGE models, DATA mapping, INFORMATION needs, NATURAL language processing, INFORMATION processing

XPathia: A Deep Learning Approach for Translating Natural Language Into XPath Queries for Non-Technical Users.

Autoren: Ahkouk, Karam Machkour, Mustapha

Quelle: International Journal of Advanced Computer Science & Applications; 2025, Vol. 16 Issue 6, p1041-1050, 10p