6 research outputs found
Pattern Based Orbit Identification for Scatterometer Level-0 Signal Images
No full textAbstractConsidering the raw data extraction algorithms designed for different sensor data this paper focuses on the SCATTEROMETER Level-0 data extracted from raw data products. An analysis of extracted signal images is shown. Then a very simple technique but a very important concept is described and implemented on such signal images. Paper shows the approach which can be help full for identification of number of orbits in to the raw data products. The main objective of this approach is to provide the statistics to the application specific user. The approach is based on analyzed behavior of signal images extracted from the raw product. This approach defines a very basic standard to identify number of orbits based on SCATTEROMETER signal images. An implementation algorithm with its time complexity is shown with the corresponding implementation in C language. The proposed approach is for signal images of Data Quality Evaluation Level-0 SCATTEROMETER signal images, but it can also be extended for other signal images. The assumption for the paper is, the scanning geometry of signal image has been established in terms of scans and pixels i.e. header or interpretation format of the signal data
A survey on Relation Extraction
No full textWith the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information. Information Extraction (IE) can extract meaningful information from text sources and present it in a structured format. The sub-tasks of IE include Named Entity Recognition (NER), Event Extraction, Relation Extraction (RE), Sentiment Extraction, Opinion Extraction, Terminology Extraction, Reference Extraction, and so on.One way to represent information in the text is in the form of entities and relations representing links between entities. The Entity Extraction task identifies entities from the text, and the Relation Extraction (RE) task can identify relationships between those entities. Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system
