On the Selection of Verbs for Natural Language Description of Traffic Scenes

1982 ◽  
pp. 22-31 ◽  
Author(s):  
Hans-Joachim Novak
Keyword(s):  
Natural Language ◽  
Selection Of ◽  
Language Description

scholarly journals Natural language description of images using hybrid recurrent neural network

2019 ◽  
Vol 9 (4) ◽  
pp. 2932
Author(s):  
Md. Asifuzzaman Jishan ◽  
Khan Raqib Mahmud ◽  
Abul Kalam Al Azad
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Text Line ◽  
Short Term ◽  
Word Representation ◽  
Benchmark Datasets ◽  
Long Short Term Memory ◽  
Language Description

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.

CONVERTING NATURAL LANGUAGE TEXT SENTENCES INTO SPN REPRESENTATIONS FOR ASSOCIATING EVENTS

2012 ◽  
Vol 06 (03) ◽  
pp. 353-370 ◽  
Author(s):  
NIKOLAOS BOURBAKIS ◽  
MICHAEL MILLS
Keyword(s):  
Natural Language ◽  
Graph Model ◽  
Stochastic Petri Nets ◽  
Natural Language Text ◽  
Multimodal Information ◽  
Efficient Processing ◽  
Functional Knowledge ◽  
Efficient Representation ◽  
Language Text ◽  
Selection Of

A better understanding of events many times requires the association and the efficient representation of multi-modal information. A good approach to this important issue is the development of a common platform for converting different modalities (such as images, text, etc.) into the same medium and associating them for efficient processing and understanding. In a previous paper we have presented a Local-Global graph model for the conversion of images into graphs with attributes and then into natural language (NL) text sentences [25]. Here, in this paper we propose the conversion of NL text sentences into graphs and then into Stochastic Petri-nets (SPN) descriptions in order to efficiently offer a model of associating "activities or changes" in multimodal information for events representation and understanding. The selection of the SPN graph model is due to its capability for efficiently representing structural and functional knowledge. Simple illustrative examples are provided for proving the concept proposed here.

Translating Terms of the Functional Basis Into Biologically Meaningful Keywords

2008 ◽  
Author(s):  
Hyunmin Cheong ◽  
L. H. Shu ◽  
Robert B. Stone ◽  
Daniel A. McAdams
Keyword(s):  
Natural Language ◽  
Engineering Design ◽  
Semantic Relations ◽  
Biological Information ◽  
Functional Basis ◽  
Design Students ◽  
Design Projects ◽  
Systematic Identification ◽  
Excellent Source ◽  
Selection Of

Biology has long been recognized as an excellent source of analogies and stimuli for engineering design. Previous work focused on the systematic identification of relevant biological analogies by searching for instances of functional keywords in biological information in natural language format. This past work revealed that engineering keywords couldn’t always be used to identify the most relevant biological analogies, as the vocabularies between biology and engineering are sufficiently distinct. Therefore, a method of identifying biologically meaningful keywords that correspond to engineering keywords was developed. Here, we apply and refine this method by generating biologically meaningful keywords for the terms of the Functional Basis, which is widely accepted as a standardized representation of the functionality of engineering products. We present insights gained on the selection of biologically meaningful keywords for the function sets based on semantic relations. We then observe the use of our keywords by providing 4th year undergraduate design students with the biologically meaningful keywords that are related to the desired functions of their design projects.

scholarly journals Linguistic processor integration for solving planimetric problems

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Natural Language ◽  
Syntactic Structure ◽  
Semantic Representation ◽  
Interactive Visualization ◽  
Subject Area ◽  
Syntactic Structures ◽  
The Subject ◽  
Language Description

The research deals with the original algorithms of the linguistic processor integration for solving planimetric problems. The linguistic processor translates the natural language description of the problem into a semantic representation based on the ontology that supports the axiomatics of geometry. The linguistic processor synthesizes natural-language comments to the solution and drawing objects. The method of interactive visualization of the linguistic processor functioning is proposed. The method provides a step-by-step dialog control of syntactic structure construction and its display in semantic representation. During the experiments, several dozens of standard syntactic structures correctly displayed in the semantic structures of the subject area were obtained. The direction of further research related to the development of the proposed approach is outlined.

Sign in / Sign up

Export Citation Format

Share Document