scholarly journals Music Suggestion Via Sentimental Analysis of User-Inputted Texts

2021 ◽  
pp. 51-66
Author(s):  
Vipin Wani ◽  
Niketan Bothe ◽  
Avani Soni
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Program ◽  
Language Processing ◽  
The State ◽  
Information Models ◽  
Specific Emotion ◽  
Opening Up

This paper overviews the state of craftsmanship in feeling acknowledgment from content and give music. Music is oftentimes alluded to as a “language of emotion”, and it is characteristic for us to classify music in terms of its enthusiastic affiliations. This paper, investigations the utilize of Natural Language Processing (NLP) for dismember the human dialect and make information models out of it. But to develop a computer program which is able give music based on text’s feeling. There may be impressive difference with respect to the recognition and translation of the feelings of a melody or uncertainty inside the piece itself. In this paper we provide a platform that tailors music according to a user-specific emotion, while also opening up the user to music they might not have perceived earlier on in life – the powers of recommendation and discovery in one piece of technology.

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.

Natural Language Processing

1996 ◽  
Vol 16 ◽  
pp. 70-85 ◽  
Author(s):  
Thomas C. Rindflesch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Domain Knowledge ◽  
The State ◽  
Point Of View ◽  
Computer Applications ◽  
Significant Progress ◽  
Future Directions

Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

A text-based multi-span network for reading comprehension

2021 ◽  
pp. 1-13
Author(s):  
Deguang Chen ◽  
Ziping Ma ◽  
Lin Wei ◽  
Yanbin Zhu ◽  
Jinlin Ma ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Market Value ◽  
The State ◽  
Experimental Results ◽  
Training Time ◽  
The Public

Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time.

scholarly journals Development and Validation of a Natural Language Processing Computer Program to Evaluate the Quality of Colonoscopy Reports

2011 ◽  
Vol 140 (5) ◽  
pp. S-413 ◽  
Author(s):  
Hendrik Harkema ◽  
Faraz Bishehsari ◽  
Evan S. Dellon ◽  
Melissa I. Saul ◽  
Wendy Chapman ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Program ◽  
Language Processing ◽  
Development And Validation

scholarly journals Automated Essay Scoring: A Survey of the State of the Art

2019 ◽  
Author(s):  
Zixuan Ke ◽  
Vincent Ng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
Educational Values ◽  
Automated Essay Scoring ◽  
Research Challenges ◽  
Essay Scoring ◽  
Made In

Despite being investigated for over 50 years, the task of automated essay scoring is far from being solved. Nevertheless, it continues to draw a lot of attention in the natural language processing community in part because of its commercial and educational values as well as the associated research challenges. This paper presents an overview of the major milestones made in automated essay scoring research since its inception.

scholarly journals The commercial NLP landscape in 2017

2017 ◽  
Vol 23 (4) ◽  
pp. 641-647 ◽  
Author(s):  
ROBERT DALE
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The State ◽  
Popular Interest

AbstractThe commercialisation of natural language processing began over 35 years ago, but it’s only in the last year or two that it’s become substantially more visible, largely because of the intense popular interest in artificial intelligence. So what’s the state of commercial NLP today? We survey the main industry categories of relevance, and offer comment on where the action is today.

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