scholarly journals An Algorithm for Solving Natural Language Query Execution Problems on Relational Databases

2012 ◽  
Vol 3 (10) ◽  
Author(s):  
Enikuomehin A. ◽  
Okwufulueze D.O
Keyword(s):  
Natural Language ◽  
Relational Databases ◽  
Query Execution ◽  
Natural Language Query

Single-shot Semantic Matching Network for Moment Localization in Videos

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Xinfang Liu ◽  
Xiushan Nie ◽  
Junya Teng ◽  
Li Lian ◽  
Yilong Yin
Keyword(s):  
Natural Language ◽  
Fixed Number ◽  
Single Shot ◽  
Semantic Matching ◽  
Matching Network ◽  
Attention Model ◽  
Semantic Relationships ◽  
Natural Language Query ◽  
Benchmark Datasets ◽  
Short Memory

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.

scholarly journals Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Miaoyuan Shi
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Manufacturing ◽  
Focus Of Attention ◽  
Knowledge Graph ◽  
Single Entity ◽  
Question And Answer ◽  
Natural Language Query ◽  
The One ◽  
Answering Questions

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.

scholarly journals Explaining Natural Language query results

2019 ◽  
Vol 29 (1) ◽  
pp. 485-508
Author(s):  
Daniel Deutch ◽  
Nave Frost ◽  
Amir Gilad
Keyword(s):  
Natural Language ◽  
Natural Language Query

Information Extraction in Biomedical Literature

2011 ◽  
pp. 615-620
Author(s):  
Min Song ◽  
Il-Yeol Song ◽  
Xiaohua Hu ◽  
Hyoil Han
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Latin American ◽  
Relational Databases ◽  
Biomedical Literature ◽  
Text Documents ◽  
Plain Text ◽  
Natural Language Text ◽  
Relational Form ◽  
Structured Representation

Information extraction (IE) technology has been defined and developed through the US DARPA Message Understanding Conferences (MUCs). IE refers to the identification of instances of particular events and relationships from unstructured natural language text documents into a structured representation or relational table in databases. It has proved successful at extracting information from various domains, such as the Latin American terrorism, to identify patterns related to terrorist activities (MUC-4). Another domain, in the light of exploiting the wealth of natural language documents, is to extract the knowledge or information from these unstructured plain-text files into a structured or relational form. This form is suitable for sophisticated query processing, for integration with relational databases, and for data mining. Thus, IE is a crucial step for fully making text files more easily accessible.

Sign in / Sign up

Export Citation Format

Share Document