scholarly journals Improving Readability of Online Privacy Policies through DOOP: A Domain Ontology for Online Privacy

Digital ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 198-215
Author(s):  
Dhiren A. Audich ◽  
Rozita Dara ◽  
Blair Nonnecke
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Ontology ◽  
Online Privacy ◽  
Privacy Policy ◽  
Proof Of Concept ◽  
Privacy Policies ◽  
Privacy Concerns ◽  
Novel Approach

Privacy policies play an important part in informing users about their privacy concerns by operating as memorandums of understanding (MOUs) between them and online services providers. Research suggests that these policies are infrequently read because they are often lengthy, written in jargon, and incomplete, making them difficult for most users to understand. Users are more likely to read short excerpts of privacy policies if they pertain directly to their concern. In this paper, a novel approach and a proof-of-concept tool are proposed that reduces the amount of privacy policy text a user has to read. It does so using a domain ontology and natural language processing (NLP) to identify key areas of the policies that users should read to address their concerns and take appropriate action. Using the ontology to locate key parts of privacy policies, average reading times were substantially reduced from 29 to 32 min to 45 s.

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.

A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
Author(s):  
M.-H. Kuo ◽  
P. Gooch ◽  
J. St-Maurice
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Emergency Room ◽  
Language Processing ◽  
Free Text ◽  
Proof Of Concept ◽  
Term Extraction ◽  
Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

scholarly journals SentiMental: An emotional profiling algorithm for identifying affect patterns in text

2018 ◽  
Author(s):  
Massimo Stella
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Algorithm ◽  
Copyright Holder ◽  
The Novel ◽  
Novel Approach ◽  
Technical Report ◽  
Potential Applications ◽  
Natural Language Processing Algorithm

This technical report outlines the mechanisms and potential applications of SentiMental, a suite of natural language processing algorithm designed and implemented by Massimo Stella, Complex Science Consulting. The following technical report briefly outlines the novel approach of SentiMental in performing sentiment and emotional analysis by directly harnessing the whole structure of the mental lexicon rather than by using affect norms. Furthermore, this technical report outlines the direct emotional profiling and the visualisations currently implemented in version 0.1 of SentiMental. Features under development and current limitations are also outlined and discussed.This technical report is not meant as a publication. The author holds full copyright and any reproduction of parts of this report must be authorised by the copyright holder. SentiMental represents a work in progress, so do not hesitate to get in touch with the author for any potential feedback.

scholarly journals Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

2014 ◽  
Vol 22 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Ching-Heng Lin ◽  
Nai-Yuan Wu ◽  
Wei-Shao Lai ◽  
Der-Ming Liou
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Annotation ◽  
Processing Application ◽  
Entry Level ◽  
Novel Approach ◽  
Clinical Document ◽  
F Measure ◽  
Document Architecture

Abstract Background and objective Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Methods Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. Results The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p&lt;0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines. Conclusions The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

scholarly journals A Model Workflow for GeoDeepDive: Locating Pliocene and Pleistocene Ice-Rafted Debris

2021 ◽  
Author(s):  
Simon Goring ◽  
Jeremiah Marsicek ◽  
Shan Ye ◽  
John Williams ◽  
Stephen Meyers ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
R Package ◽  
Regular Expressions ◽  
Learning Technology ◽  
Proof Of Concept ◽  
Spatiotemporal Information ◽  
Age Estimates

Machine learning technology promises a more efficient and scalable approach to locating and aggregating data and information from the burgeoning scientific literature. Realizing this promise requires provision of applications, data resources, and the documentation of analytic workflows. GeoDeepDive provides a digital library comprising over 13 million peer-reviewed documents and the computing infrastructure upon which to build and deploy search and text-extraction capabilities using regular expressions and natural language processing. Here we present a model GeoDeepDive workflow and accompanying R package to show how GeoDeepDive can be employed to extract spatiotemporal information about site-level records in the geoscientific literature. We apply these capabilities to a proof-of-concept subset of papers in a case study to generate a preliminary distribution of ice-rafted debris (IRD) records in both space and time. We use regular expressions and natural language-processing utilities to extract and plot reliable latitude-longitude pairs from publications containing IRD, and also extract age estimates from those publications. This workflow and R package provides researchers from the geosciences and allied disciplines a general set of tools for querying spatiotemporal information from GeoDeepDive for their own science questions.

scholarly journals From Prescription to Description: Mapping the GDPR to a Privacy Policy Corpus Annotation Scheme

2020 ◽  
Author(s):  
Ellen Poplavska ◽  
Thomas B. Norton ◽  
Shomir Wilson ◽  
Norman Sadeh
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Specific Information ◽  
Privacy Policy ◽  
Legal Research ◽  
Privacy Policies ◽  
Annotation Scheme ◽  
General Data Protection Regulation ◽  
Data Practices ◽  
General Data

The European Union’s General Data Protection Regulation (GDPR) has compelled businesses and other organizations to update their privacy policies to state specific information about their data practices. Simultaneously, researchers in natural language processing (NLP) have developed corpora and annotation schemes for extracting salient information from privacy policies, often independently of specific laws. To connect existing NLP research on privacy policies with the GDPR, we introduce a mapping from GDPR provisions to the OPP-115 annotation scheme, which serves as the basis for a growing number of projects to automatically classify privacy policy text. We show that assumptions made in the annotation scheme about the essential topics for a privacy policy reflect many of the same topics that the GDPR requires in these documents. This suggests that OPP-115 continues to be representative of the anatomy of a legally compliant privacy policy, and that the legal assumptions behind it represent the elements of data processing that ought to be disclosed within a policy for transparency. The correspondences we show between OPP-115 and the GDPR suggest the feasibility of bridging existing computational and legal research on privacy policies, benefiting both areas.

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