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.

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

scholarly journals A Primer on Neural Network Models for Natural Language Processing

2016 ◽  
Vol 57 ◽  
pp. 345-420 ◽  
Author(s):  
Yoav Goldberg
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Processing ◽  
Network Models ◽  
Neural Network Models ◽  
Convolutional Networks ◽  
The Past ◽  
Gradient Computation

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

scholarly journals Finite-state methods and models in natural language processing

2011 ◽  
Vol 17 (2) ◽  
pp. 141-144
Author(s):  
ANSSI YLI-JYRÄ ◽  
ANDRÁS KORNAI ◽  
JACQUES SAKAROVITCH
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Current Issue ◽  
Special Issue ◽  
The Public ◽  
The Past ◽  
Finite State ◽  
Final Selection

For the past two decades, specialised events on finite-state methods have been successful in presenting interesting studies on natural language processing to the public through journals and collections. The FSMNLP workshops have become well-known among researchers and are now the main forum of the Association for Computational Linguistics' (ACL) Special Interest Group on Finite-State Methods (SIGFSM). The current issue on finite-state methods and models in natural language processing was planned in 2008 in this context as a response to a call for special issue proposals. In 2010, the issue received a total of sixteen submissions, some of which were extended and updated versions of workshop papers, and others which were completely new. The final selection, consisting of only seven papers that could fit into one issue, is not fully representative, but complements the prior special issues in a nice way. The selected papers showcase a few areas where finite-state methods have less than obvious and sometimes even groundbreaking relevance to natural language processing (NLP) applications.

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.

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