scholarly journals Review of neural approaches for conditional text generation

2021 ◽  
pp. 102-107
Author(s):  
O. H. Skurzhanskyi ◽  
A. A. Marchenko
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Language Processing ◽  
Test Generation ◽  
State Of The Art ◽  
Text Generation ◽  
Text Simplification ◽  
Local Sequence ◽  
Key Aspects ◽  
Paraphrase Generation

The article is devoted to the review of conditional test generation, one of the most promising fields of natural language processing and artificial intelligence. Specifically, we explore monolingual local sequence transduction tasks: paraphrase generation, grammatical and spelling errors correction, text simplification. To give a better understanding of the considered tasks, we show examples of good rewrites. Then we take a deep look at such key aspects as publicly available datasets with the splits (training, validation, and testing), quality metrics for proper evaluation, and modern solutions based primarily on modern neural networks. For each task, we analyze its main characteristics and how they influence the state-of-the-art models. Eventually, we investigate the most significant shared features for the whole group of tasks in general and for approaches that provide solutions for them.

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

A Matter of Perspective

2020 ◽  
pp. 182-202
Author(s):  
Katie Miller
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Human Rights ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Equal Opportunity ◽  
Facial Recognition ◽  
Phone Service

The challenge presented is an age when some decisions are made by humans, some are made by AI, and some are made by a combination of AI and humans. For the person refused housing, a phone service, or employment, the experience is the same, but the ability to understand what has happened and obtain a remedy may be very different if the discrimination is attributable to or contributed by an AI system. If we are to preserve the policy intentions of our discrimination, equal opportunity, and human rights laws, we need to understand how discrimination arises in AI systems; how design in AI systems can mitigate such discrimination; and whether our existing laws are adequate to address discrimination in AI. This chapter endeavours to provide this understanding. In doing so, it focuses on narrow but advanced forms of artificial intelligence, such as natural language processing, facial recognition, and cognitive neural networks.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals Use of artificial intelligence in analytical systems for the clinical laboratory

1995 ◽  
Vol 17 (1) ◽  
pp. 1-15 ◽  
Author(s):  
John F. Place ◽  
Alain Truchaud ◽  
Kyoichi Ozawa ◽  
Harry Pardue ◽  
Paul Schnipelsky
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Expert Systems ◽  
Language Processing ◽  
Knowledge Engineering ◽  
Clinical Laboratory ◽  
Failure Detection ◽  
Domain Experts ◽  
Clinical Laboratories ◽  
The Neural Network

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks.This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system.In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories.It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.

scholarly journals TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 551-556 ◽  
Author(s):  
V. Palma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Mobile App ◽  
Information Modeling ◽  
Computational Power ◽  
Architectural Heritage ◽  
Research Stream ◽  
Heritage Collections

<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.</p>

scholarly journals Syntax-Guided Controlled Generation of Paraphrases

2020 ◽  
Vol 8 ◽  
pp. 330-345
Author(s):  
Ashutosh Kumar ◽  
Kabir Ahuja ◽  
Raghuram Vadapalli ◽  
Partha Talukdar
Keyword(s):  
Real World ◽  
English Language ◽  
State Of The Art ◽  
Source Code ◽  
Future Research ◽  
Text Generation ◽  
Syntactic Information ◽  
Input Sentence ◽  
World English ◽  
Paraphrase Generation

Given a sentence (e.g., “I like mangoes”) and a constraint (e.g., sentiment flip), the goal of controlled text generation is to produce a sentence that adapts the input sentence to meet the requirements of the constraint (e.g., “I hate mangoes”). Going beyond such simple constraints, recent work has started exploring the incorporation of complex syntactic-guidance as constraints in the task of controlled paraphrase generation. In these methods, syntactic-guidance is sourced from a separate exemplar sentence. However, these prior works have only utilized limited syntactic information available in the parse tree of the exemplar sentence. We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation. We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance. We perform extensive automated and human evaluations over multiple real-world English language datasets to demonstrate the efficacy of Sgcp over state-of-the-art baselines. To drive future research, we have made Sgcp’s source code available. 1

scholarly journals Convolutional Neural Networks Inference Memory Optimization with Receptive Field-Based InputTiling

2021 ◽  
Author(s):  
Weihao Zhuang ◽  
Tristan Hascoet ◽  
Xunquan Chen ◽  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
State Of The Art ◽  
Input Image ◽  
Memory Consumption ◽  
Excellent Performance ◽  
Conceptual Approach ◽  
Recent Developments

Abstract Currently, deep learning plays an indispensable role in many fields, including computer vision, natural language processing, and speech recognition. Convolutional Neural Networks (CNNs) have demonstrated excellent performance in computer vision tasks thanks to their powerful feature extraction capability. However, as the larger models have shown higher accuracy, recent developments have led to state-of-the-art CNN models with increasing resource consumption. This paper investigates a conceptual approach to reduce the memory consumption of CNN inference. Our method consists of processing the input image in a sequence of carefully designed tiles within the lower subnetwork of the CNN, so as to minimize its peak memory consumption, while keeping the end-to-end computation unchanged. This method introduces a trade-off between memory consumption and computations, which is particularly suitable for high-resolution inputs. Our experimental results show that MobileNetV2 memory consumption can be reduced by up to 5.3 times with our proposed method. For ResNet50, one of the most commonly used CNN models in computer vision tasks, memory can be optimized by up to 2.3 times.

scholarly journals Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

2019 ◽  
Vol 9 (6) ◽  
pp. 1143 ◽  
Author(s):  
Sevinj Yolchuyeva ◽  
Géza Németh ◽  
Bálint Gyires-Tóth
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Speech Synthesis ◽  
State Of The Art ◽  
Error Rates ◽  
Written Form ◽  
G2p Conversion

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.

scholarly journals The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6428
Author(s):  
Ronan Thenault ◽  
Kevin Kaulanjan ◽  
Thomas Darde ◽  
Nathalie Rioux-Leclercq ◽  
Karim Bensalah ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Language Processing ◽  
Daily Practice ◽  
Skills Training ◽  
Treatment Modalities ◽  
Cancer Management ◽  
Potential Applications

Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.

scholarly journals Design Challenges for Entity Linking

2015 ◽  
Vol 3 ◽  
pp. 315-328 ◽  
Author(s):  
Xiao Ling ◽  
Sameer Singh ◽  
Daniel S. Weld
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Data Sets ◽  
Entity Linking ◽  
Joint Inference ◽  
Extensive Evaluation ◽  
Key Aspects ◽  
Design Challenges

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.

Sign in / Sign up

Export Citation Format

Share Document