scholarly journals Ontology-Assisted Database Integration to Support Natural Language Processing and Biomedical Data-mining

2004 ◽  
Vol 1 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Jean-Luc Verschelde ◽  
Mariana Casella Dos Santos ◽  
Tom Deray ◽  
Barry Smith ◽  
Werner Ceusters
Keyword(s):  
Data Mining ◽  
Natural Language ◽  
Language Processing ◽  
Biomedical Ontology ◽  
Biological Data ◽  
Heterogeneous Databases ◽  
Database Integration ◽  
Biomedical Data ◽  
Biological Phenomena ◽  
Different Levels

Summary Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language.

The Future of Instruction Modeling

2020 ◽  
pp. 205-228
Author(s):  
George A. Khachatryan
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Foreign Language ◽  
Natural Language ◽  
Blended Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Government Policies ◽  
Free Form ◽  
Learning Programs

Instruction modeling is still in its early stages. This chapter discusses promising directions in which instruction modeling could develop in coming years. This includes increasing the richness of interfaces used in instruction modeling programs (e.g., by allowing students to enter responses in free form and have them graded via natural language processing); applying instruction modeling to subjects beyond mathematics, including English, foreign language, and science; using educational data mining to create automated “coaches” to help teachers better implement instruction modeling programs in their classrooms; creating approaches to instruction modeling that allow for rapid authorship of content; redesigning schools (in schedules as well as architecture) to optimize the use of instruction modeling; and putting in place government policies to encourage the use of comprehensive blended learning programs (such as those developed through instruction modeling).

scholarly journals Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods

Cancer ◽  
2016 ◽  
Vol 123 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Tejal A. Patel ◽  
Mamta Puppala ◽  
Richard O. Ogunti ◽  
Joe E. Ensor ◽  
Tiancheng He ◽  
...  
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Clinical Decision ◽  
Pathologic Findings ◽  
Mining Methods

COMPUTATIONAL HUMANITIES

2018 ◽  
Vol 8 (6) ◽  
pp. 45
Author(s):  
NANA AMPAH ◽  
Matthew Sadiku ◽  
Omonowo Momoh ◽  
Sarhan Musa
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Analysis Data ◽  
Large Database ◽  
Database Analysis ◽  
Computational Tools ◽  
The Past ◽  
Network Mapping

Computational humanities is at the intersection of computing technologies and the disciplines of the humanities. Research in this field has steadily increased over the past years. Computational tools supporting textual search, large database analysis, data mining, network mapping, and natural language processing are employed by the humanities researcher.  This opens up new realms for analysis and understanding.  This paper provides a brief introduction into computational humanities.

scholarly journals Toward Requirements and Design Traceability Using Natural Language Processing

2018 ◽  
Vol 3 (7) ◽  
pp. 42 ◽  
Author(s):  
Omer Salih Dawood ◽  
Abd-El-Kader Sahraoui
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Unified Modeling Language ◽  
Rule Engine ◽  
Natural Languages ◽  
Unified Modeling ◽  
Requirement Document ◽  
Different Levels

The paper aimed to address the problem of incompleteness and inconsistency between requirements and design stages, and how to make efficient linking between these stages. Software requirements written in natural languages (NL), Natural Language Processing (NLP) can be used to process requirements. In our research we built a framework that can be used to generate design diagrams from requirements in semi-automatic way, and make traceability between requirements and design phases, and in contrast. Also framework shows how to manage traceability in different levels, and how to apply changes to different artifacts. Many traceability reports can be generated based on developed framework. After Appling this model we obtained good results. Based on our case study the model generate a class diagram depends on central rule engine, and traceability was built and can be managed in visualize manner. We proposed to continue this research as its very critical area by adding more Unified Modeling Language(UML) diagrams, and apply changes directly inside software requirement document.

DATA MINING WITH NATURAL LANGUAGE PROCESSING AND CORPUS LINGUISTICS

2016 ◽  
pp. 255-275 ◽  
Author(s):  
Alison L. Bailey ◽  
Anne Blackstock-Bernstein ◽  
Eve Ryan ◽  
Despina Pitsoulakis
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Corpus Linguistics

scholarly journals Review On: Detection of Spam Comments Using NLP Algorithm

2018 ◽  
Vol 7 (01) ◽  
pp. 23386-23489
Author(s):  
Miss Rohini D.Warkar ◽  
Mr.I.R. Shaikh
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Social Networking ◽  
Real Time ◽  
Social Services ◽  
Language Processing ◽  
Social Networking Sites ◽  
Online Media

Detecting trending topics is perfect to summarize information getting from social media. To extract what topic is becoming hot on online media is one of the challenges. As we considering social media so social services are opportunity for spamming which greatly affect on value of real time search. Therefore the next task is to control spamming from social networking sites. For completing these challenges different concepts of data mining will be used. For now whatever work has been done is narrated below like spam control using natural language processing for preprocessing and clustering. One account has been created for making it real.

Data Mining

2012 ◽  
pp. 75-94 ◽  
Author(s):  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Pattern Mining ◽  
Data Sources ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

Data Mining provides approaches for the identification and discovery of non-trivial patterns and models hidden in large collections of data. In the applied natural language processing domain, data mining usually requires preprocessed data that has been extracted from textual documents. Additionally, this data is often integrated with other data sources. This chapter provides an overview on data mining focusing on approaches for pattern mining, cluster analysis, and predictive model construction. For those, we discuss exemplary techniques that are especially useful in the applied natural language processing context. Additionally, we describe how the presented data mining approaches are connected to text mining, text classification, and clustering, and discuss interesting problems and future research directions.

Mining Meaning

2011 ◽  
pp. 125-140
Author(s):  
William L. Tullar
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Group Discussions ◽  
Text Data ◽  
Virtual Group ◽  
Extraction Step ◽  
Using Data ◽  
Lower Frequencies

This chapter focuses on the pattern detection and extraction step in text data commonly called text data mining. I examine some of the literature on natural language processing and propose a method of recovering value from the text of virtual group discussions based on methods derived from the communication field. Then, I apply the method in a case using data from 216 different groups from a virtual group experiment. The results from the case show that higher performing groups are characterized by higher frequencies of acts of dominance and higher frequencies of terms concerning cognition, communication and praise. Higher performing groups were also characterized by lower frequencies of acts of equivalence and lower frequencies of leveling terms and numerical terms. Ways to use this knowledge to improve the groups’ performance are discussed.

Sign in / Sign up

Export Citation Format

Share Document