Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference

2016 ◽  
pp. 515-526
Author(s):  
Zhao Meng ◽  
Lili Mou ◽  
Ge Li ◽  
Zhi Jin
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Convolutional Neural Networks ◽  
Context Aware

scholarly journals COMPARISON OF PARSING METHODS FOR THE ANALISYS OF A NATURAL LANGUAGE SENTENCE

2021 ◽  
Vol 10 (34) ◽  
Author(s):  
A.N SAK ◽  
◽  
E.V BESSONOVA ◽  
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Machine Translation ◽  
Convolutional Neural Networks ◽  
Object Oriented ◽  
Important Task ◽  
Object Oriented Programming ◽  
Parts Of Speech ◽  
Translation Systems

When constructing machine translation systems, an important task is to represent data using graphs, where words act as vertices, and relations between words in a sentence act as edges. One of these tasks at the first stage of the analysis is the classification of words as parts of speech, and at the next stage of the analysis to determine the belonging of words to the sentence members’ classes. The article discusses methods of parsing both on the basis of rules determined in advance by means of traditional object-oriented programming, and on the basis of analysis by means of graph convolutional neural networks with their subsequent training. Online dictionaries act as a thesaurus.

scholarly journals Towards Task Understanding in Visual Settings

2019 ◽  
Vol 33 ◽  
pp. 10027-10028 ◽  
Author(s):  
Sebastin Santy ◽  
Wazeer Zulfikar ◽  
Rishabh Mehrotra ◽  
Emine Yilmaz
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Visual Images ◽  
Text Generation ◽  
Image Captioning ◽  
Task Descriptions ◽  
Task Ontology ◽  
Task Understanding

We consider the problem of understanding real world tasks depicted in visual images. While most existing image captioning methods excel in producing natural language descriptions of visual scenes involving human tasks, there is often the need for an understanding of the exact task being undertaken rather than a literal description of the scene. We leverage insights from real world task understanding systems, and propose a framework composed of convolutional neural networks, and an external hierarchical task ontology to produce task descriptions from input images. Detailed experiments highlight the efficacy of the extracted descriptions, which could potentially find their way in many applications, including image alt text generation.

scholarly journals Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

2017 ◽  
Vol 4 (04) ◽  
pp. 1 ◽  
Author(s):  
Babak Ehteshami Bejnordi ◽  
Guido Zuidhof ◽  
Maschenka Balkenhol ◽  
Meyke Hermsen ◽  
Peter Bult ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Context Aware ◽  
Breast Carcinomas ◽  
Histopathology Images
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