A framework for relationship extraction from unstructured text via link grammar parsing

2018 ◽  
Author(s):  
Onur Savas ◽  
Ken Samuel ◽  
Vikram Manikonda
Keyword(s):  
Unstructured Text ◽  
Relationship Extraction ◽  
Link Grammar

scholarly journals Extracting Conceptual Relationships and Inducing Concept Lattices from Unstructured Text

2019 ◽  
Vol 28 (4) ◽  
pp. 669-681
Author(s):  
V.S. Anoop ◽  
S. Asharaf
Keyword(s):  
Machine Learning Algorithms ◽  
Formal Concept ◽  
Text Data ◽  
Concept Lattices ◽  
Plain Text ◽  
Concept Extraction ◽  
Unstructured Text ◽  
Relationship Extraction ◽  
Real World Datasets ◽  
Conceptual Relationships

Abstract Concept and relationship extraction from unstructured text data plays a key role in meaning aware computing paradigms, which make computers intelligent by helping them learn, interpret, and synthesis information. These concepts and relationships leverage knowledge in the form of ontological structures, which is the backbone of semantic web. This paper proposes a framework that extracts concepts and relationships from unstructured text data and then learns lattices that connect concepts and relationships. The proposed framework uses an off-the-shelf tool for identifying common concepts from a plain text corpus and then implements machine learning algorithms for classifying common relations that connect those concepts. Formal concept analysis is then used for generating concept lattices, which is a proven and principled method of creating formal ontologies that aid machines to learn things. A rigorous and structured experimental evaluation of the proposed method on real-world datasets has been conducted. The results show that the newly proposed framework outperforms state-of-the-art approaches in concept extraction and lattice generation.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

Developing Name Entity Recognition for Structured and Unstructured Text Formatting Dataset

2020 ◽  
Author(s):  
Nadhia Salsabila Azzahra ◽  
Muhammad Okky Ibrohim ◽  
Junaedi Fahmi ◽  
Bagus Fajar Apriyanto ◽  
Oskar Riandi
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Unstructured Text ◽  
Name Entity
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