Margin of Safety in TMDLs: Natural Language Processing-Aided Review of the State of Practice

2020 ◽  
Vol 25 (4) ◽  
pp. 04020002 ◽  
Author(s):  
Robert Nunoo ◽  
Paul Anderson ◽  
Saurav Kumar ◽  
Jun-Jie Zhu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The State ◽  
Margin Of Safety ◽  
State Of Practice

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.

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.

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 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.

Natural Language Processing for Requirements Engineering

2021 ◽  
Vol 54 (3) ◽  
pp. 1-41
Author(s):  
Liping Zhao ◽  
Waad Alhoshan ◽  
Alessio Ferrari ◽  
Keletso J. Letsholo ◽  
Muideen A. Ajagbe ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Requirements Engineering ◽  
Language Processing ◽  
New Technologies ◽  
The State ◽  
Mapping Study ◽  
Pos Tagging ◽  
Starting Point ◽  
Holistic Understanding

Natural Language Processing for Requirements Engineering (NLP4RE) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools, and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts, and establishing requirements traceability links. This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Our main results are as follows: (i) we identify a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assessed in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 17 of them (13.08%) are available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools, and 25 NLP resources, but most of them—particularly those novel NLP techniques and specialized tools—are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). The mapping study not only provides a collection of the literature in NLP4RE but also, more importantly, establishes a structure to frame the existing literature through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, highlight technology transfer needs, and encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.

An Overview of Shallow and Deep Natural Language Processing for Ontology Learning

2011 ◽  
pp. 16-37 ◽  
Author(s):  
Amal Zouaq
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Process ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Knowledge Extraction ◽  
The State ◽  
Ontology Learning ◽  
Analysis Methods

This chapter gives an overview over the state-of-the-art in natural language processing for ontology learning. It presents two main NLP techniques for knowledge extraction from text, namely shallow techniques and deep techniques, and explains their usefulness for each step of the ontology learning process. The chapter also advocates the interest of deeper semantic analysis methods for ontology learning. In fact, there have been very few attempts to create ontologies using deep NLP. After a brief introduction to the main semantic analysis approaches, the chapter focuses on lexico-syntactic patterns based on dependency grammars and explains how these patterns can be considered as a step towards deeper semantic analysis. Finally, the chapter addresses the “ontologization” task that is the ability to filter important concepts and relationships among the mass of extracted knowledge.

Morphological disambiguation of Hebrew: a case study in classifier combination

2012 ◽  
Vol 20 (1) ◽  
pp. 69-97 ◽  
Author(s):  
GENNADI LEMBERSKY ◽  
DANNY SHACHAM ◽  
SHULY WINTNER
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Morphological Analysis ◽  
State Of The Art ◽  
The State ◽  
Classifier Combination ◽  
Complex Morphology ◽  
Percent Accuracy

AbstractMorphological analysis and disambiguation are crucial stages in a variety of natural language processing applications, especially when languages with complex morphology are concerned. We present a system which disambiguates the output of a morphological analyzer for Hebrew. It consists of several simple classifiers and a module that combines them under the constraints imposed by the analyzer. We explore several approaches to classifier combination, as well as a back-off mechanism that relies on a large unannotated corpus. Our best result, around 83 percent accuracy, compares favorably with the state of the art on this task.

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