Critique on Cache Transition Techniques for Semantic Graph Parsing for optimizing Search Process using Text Mining

This paper elaborates the transition system that gives the standard transition-based dependency parsing techniques for generating the graph. It is essential to know the standard transition techniques for all graphical problems. Cache transition technique plays a vital role in optimizing the search process in various text mining applications. This paper provides an overview on cache transition technique for parsing semantic graphs for several Natural Language Processing (NLP) applications. According to this paper, the cache is having the fixed size m, by tree decomposition theory according to which there is a relationship between the parameter m and class of graphs produced by the theory

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Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

Human Systems Management ◽

10.3233/hsm-211179 ◽

2021 ◽

pp. 1-13

Author(s):

Lamiae Benhayoun ◽

Daniel Lang

Keyword(s):

Artificial Intelligence ◽

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Academic Training ◽

Market Requirements ◽

Job Advertisements ◽

The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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Identifying Causality and Contributory Factors of Pipeline incidents by Employing Natural Language Processing and Text Mining Techniques

Process Safety and Environmental Protection ◽

10.1016/j.psep.2021.05.036 ◽

2021 ◽

Author(s):

Guanyang Liu ◽

Mason Boyd ◽

Mengxi Yu ◽

S. Zohra Halim ◽

Noor Quddus

Keyword(s):

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Contributory Factors

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Application of Natural Language Processing and Text Mining to Identify Patterns in Construction-Defect Litigation Cases

Journal of Legal Affairs and Dispute Resolution in Engineering and Construction ◽

10.1061/(asce)la.1943-4170.0000308 ◽

2019 ◽

Vol 11 (4) ◽

pp. 04519024 ◽

Cited By ~ 4

Author(s):

Yashovardhan Jallan ◽

Elizabeth Brogan ◽

Baabak Ashuri ◽

Caroline M. Clevenger

Keyword(s):

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Language Processing

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Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Journal of Medical Internet Research ◽

10.2196/20773 ◽

2020 ◽

Vol 22 (8) ◽

pp. e20773 ◽

Cited By ~ 1

Author(s):

Antoine Neuraz ◽

Ivan Lerner ◽

William Digan ◽

Nicolas Paris ◽

Rosy Tsopra ◽

...

Keyword(s):

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Calcium Channel ◽

Language Processing ◽

Calcium Channel Blockers ◽

Structured Data ◽

Channel Blockers ◽

Knowledge Model ◽

Long Term Treatment

Background A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

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Identificação de Pragas e Doenças na Cultura da Soja por meio de um Sistema Computacional em Linguagem Natural

10.14210/cotb.v12.p324-331 ◽

2021 ◽

Author(s):

Carolinne Roque e Faria ◽

Cinthyan Renata Sachs Camerlengo de Barb

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Computer System ◽

Language Processing ◽

Agricultural Area ◽

Syntactic Analysis ◽

Dependency Parsing ◽

Named Entities ◽

Pests And Diseases ◽

Improve Production

Technology is becoming expressively popular among agribusiness producers and is progressing in all agricultural area. One of the difficulties in this context is to handle data in natural language to solve problems in the field of agriculture. In order to build up dialogs and provide rich researchers, the present work uses Natural Language Processing (NLP) techniques to develop an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in the soybean farming, stored in a database repository to provide accurate diagnoses to simplify the work of the agricultural professional and also for those who deal with a lot of information in this area. Information on 108 pests and 19 diseases that damage Brazilian soybean was collected from Brazilian bibliographic manuals with the purpose to optimize the data and improve production, using the spaCy library for syntactic analysis of NLP, which allowed the pre-process the texts, recognize the named entities, calculate the similarity between the words, verify dependency parsing and also provided the support for the development requirements of the CAROLINA tool (Robotized Agronomic Conversation in Natural Language) using the language belonging to the agricultural area.

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