scholarly journals Further Frontiers in GIS: Extending Spatial Analysis to Textual Sources in Archaeology

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Patricia Murrieta-Flores ◽  
Ian Gregory
Keyword(s):  
Natural Language Processing ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Corpus Linguistics ◽  
Geographic Information ◽  
Processing Techniques

AbstractAlthough the use of Geographic Information Systems (GIS) has a long history in archaeology, spatial technologies have been rarely used to analyse the content of textual collections. A newly developed approach termed Geographic Text Analysis (GTA) is now allowing the semi-automated exploration of large corpora incorporating a combination of Natural Language Processing techniques, Corpus Linguistics, and GIS. In this article we explain the development of GTA, propose possible uses of this methodology in the field of archaeology, and give a summary of the challenges that emerge from this type of analysis.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

An approach to neural network analysis of text information in the economic assessment of companies

2021 ◽  
Vol 20 (8) ◽  
pp. 1574-1594
Author(s):  
Aleksandr R. NEVREDINOV
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Economic Assessment ◽  
Management Decision ◽  
Textual Information ◽  
Financial Condition ◽  
Analysis And Synthesis ◽  
Management Decision Making

Subject. When evaluating enterprises, maximum accuracy and comprehensiveness of analysis are important, although the use of various indicators of organization’s financial condition and external factors provide a sufficiently high accuracy of forecasting. Many researchers are increasingly focusing on the natural language processing to analyze various text sources. This subject is extremely relevant against the needs of companies to quickly and extensively analyze their activities. Objectives. The study aims at exploring the natural language processing methods and sources of textual information about companies that can be used in the analysis, and developing an approach to the analysis of textual information. Methods. The study draws on methods of analysis and synthesis, systematization, formalization, comparative analysis, theoretical and methodological provisions contained in domestic and foreign scientific works on text analysis, including for purposes of company evaluation. Results. I offer and test an approach to using non-numeric indicators for company analysis. The paper presents a unique model, which is created on the basis of existing developments that have shown their effectiveness. I also substantiate the use of this approach to analyze a company’s condition and to include the analysis results in models for overall assessment of the state of companies. Conclusions. The findings improve scientific and practical understanding of techniques for the analysis of companies, the ways of applying text analysis, using machine learning. They can be used to support management decision-making to automate the analysis of their own and other companies in the market, with which they interact.

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