NLP-based Approach Research Articles

Technical solution to counter potential crime: Text analysis to detect fake news and disinformation

Fake news detection is a challenging and complex task. Yet, several approaches to deal with this problem have already been proposed. The majority of solutions employ the NLP-based approach, where various architectures of a deep artificial neural network are proposed. However, as the experiments show, different NLP-based solutions have great performance in a single domain, but transferring them to another one is tedious. Therefore, in this paper, we propose a hybrid approach to dealing with this problem. Instead of retraining one big model on different types of data, we bundle several smaller models using meta-learning techniques. This paper is an extension of our previous research presented in Kula et al. (2021).

An Nlp-Based Approach for Identifying Microservices from a Set of Business Processes

An Nlp-Based Approach for Identifying Microservices from a Set of Business Processes

RCM-extractor: an automated NLP-based approach for extracting a semi formal representation model from natural language requirements

RCM-extractor: an automated NLP-based approach for extracting a semi formal representation model from natural language requirements

Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic.

IntroductionCOVID-19 exposed systemic gaps with increased potential for diagnostic error. This project implemented a new approach leveraging electronic safety reporting to identify and categorize diagnostic errors during the pandemic.MethodsAll safety event reports from March 1, 2020, to February 28, 2021, at an academic medical center were evaluated using two complementary pathways (Pathway 1: all reports with explicit mention of COVID-19; Pathway 2: all reports without explicit mention of COVID-19 where natural language processing [NLP] plus logic-based stratification was applied to identify potential cases). Cases were evaluated by manual review to identify diagnostic error/delay and categorize error type using a recently proposed classification framework of eight categories of pandemic-related diagnostic errors.ResultsA total of 14,230 reports were included, with 95 (0.7%) identified as cases of diagnostic error/delay. Pathway 1 (n = 1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV] = 2.5%), of which 35.6% (16/45) were attributed to pandemic-related strain. In Pathway 2, the NLP–based algorithm flagged 110 safety reports for manual review from 12,450 eligible reports. Of these, 50 reports had diagnostic error/delay (PPV = 45.5%); 94.0% (47/50) were related to strain. Errors from all eight categories of the taxonomy were found on analysis.ConclusionAn event reporting–based strategy including use of simple-NLP–identified COVID-19–related diagnostic errors/delays uncovered several safety concerns related to COVID-19. An NLP–based approach can complement traditional reporting and be used as a just-in-time monitoring system to enable early detection of emerging risks from large volumes of safety reports.

PNS98 Natural Language Processing (NLP)-Based Detection of Transgender and Gender Non-Conforming Patients in Electronic Health Record (EHR)-Derived Data

Transgender and gender non-conforming (GNC) individuals are known to have inferior healthcare outcomes compared to their cisgender peers. However, studying this population in EHR-derived data is challenging as gender identity indicators (e.g., ICD codes, identity status drop-downs) are not reliably populated. We sought to remedy this by developing an NLP-based approach to detect transgender and GNC patients in a real-world dataset, benchmarking model performance against the use of ICD codes.

The effect of NLP-based approach to teaching surgical procedures to senior OBGYN residents

The effect of NLP-based approach to teaching surgical procedures to senior OBGYN residents

User generated content for exploring factors affecting intention to use travel and food delivery services

Abstract Customer reviews/comments on product-websites and on social-media pages can serve as great information sources for both customers and service-providers. For exploring the drivers of usage intention in context of Online-Food-Delivery services (OFDs) and Online-Travel-Agency services (OTAs), traditional-based (qualitative or quantitative or mixed-method) approaches may not be enough. This study utilizes a multi-method approach comprising of both traditional and Natural-Language-Processing (NLP)-based approaches. This study has captured the emic-perspectives using qualitative semi-structured-interviews and etic-perspectives using NLP-based analysis of extant literature. The conceptual model developed from the model of online-decision-making stance, was tested quantitatively using survey data and by NLP-based approach using online user reviews. The path-model was tested using Structural-Equation-Modeling (SEM). Results of this study reveal that price benefits and trust-in-service are major predictors of customer’s usage intention in OFD and OTA contexts. The study concludes with the various implications, limitations and future directions.

NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness.

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for time-step window given past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence (seq2seq) deep-learning machine translation algorithms is presented. In addition, a custom Seq2Seq convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher () forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher () validation accuracy indicative of less over-fitting with the same amount of available training data. The real-world application of the proposed NLP modeling of industrial HMIs, such as in power generating stations control rooms, aviation (cockpits), and so forth, is towards the realization of a non-intrusive operator situational awareness monitoring framework through prediction of HMI states.

Ontology-Driven Real World Evidence Extraction from Clinical Narratives.

Unstructured clinical notes contain a huge amount of information. We investigated the possibility of harvesting such information through an NLP-based approach. A manually curated ontology is the only resource required to handle all the steps of the process leading from clinical narrative to a structured data warehouse (i2b2). We have tested our approach at the Papa Giovanni XXIII hospital in Bergamo (Italy) on pathology reports collected since 2008.

Predicting User Motivation Towards Retention of e-Services: An NLP-based Approach

In this modern era, the dynamic business world has led to the emergence of ‘market orientation’ and ‘social CRM’ (Kohli & Jaworski, 1990; Narver & Slater, 1990; Diffley et al., 2018). The benefits of e-Services are often not fully utilized because of users’ unwillingness to use it (Venkatesh & Davis, 2000; Devaraj & Kohli, 2003). Hence, understanding the user’s motivation in an e-service through Twitter data can help companies form better strategies to retain users. Though people adopt services quickly, they tend to discontinue the service after limited use. Productivity benefits and maximum customer lifetime value (CLV) are typically obtained in the continued use phase (Venkatesh et al., 2003; Kim & Malhotra, 2005).With the emergence of social media, extracting and processing information (Kosala & Blockeel, 2000, Sakaki et al., 2010, Russell, 2011; Crooks et al., 2013), can help in understanding the motivation of users (DeVaro et al., 2018) towards using an eService. This has motivated us to analyze Twitter data to understand customer motivation levels in eService retention. In this study, 1000 tweets were downloaded from ten different e-Service providers based on the company’s official twitter handle and analyzed. The results show that using NaA¯ve Bayes on function and content words help in predicting retention intention. Predicting the IS continuance intention of users through tweets analysis can help companies perform better sentiment analysis and provide customized benefits to users. The limitation of this study is that though Twitter analytics can serve a good medium for analyzing users’ behavioural intentions, the words and its usage in different situations can change.

Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data.

Diet and physical activity are known as important lifestyle factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used to measure physical activity or detect eating time. In many intervention studies, however, stringent monitoring of overall dietary composition and energy intake is needed. Currently, such a monitoring relies on self-reported data by either entering text or taking an image that represents food intake. These approaches suffer from limitations such as low adherence in technology adoption and time sensitivity to the diet intake context. In order to address these limitations, we introduce development and validation of Speech2Health, a voice-based mobile nutrition monitoring system that devises speech processing, natural language processing (NLP), and text mining techniques in a unified platform to facilitate nutrition monitoring. After converting the spoken data to text, nutrition-specific data are identified within the text using an NLP-based approach that combines standard NLP with our introduced pattern mapping technique. We then develop a tiered matching algorithm to search the food name in our nutrition database and accurately compute calorie intake values. We evaluate Speech2Health using real data collected with 30 participants. Our experimental results show that Speech2Health achieves an accuracy of 92.2% in computing calorie intake. Furthermore, our user study demonstrates that Speech2Health achieves significantly higher scores on technology adoption metrics compared to text-based and image-based nutrition monitoring. Our research demonstrates that new sensor modalities such as voice can be used either standalone or as a complementary source of information to existing modalities to improve the accuracy and acceptability of mobile health technologies for dietary composition monitoring.

NLP-Based Approach to Semantic Classification of Heterogeneous Transportation Asset Data Terminology

AbstractThe inconsistency of data terminology has imposed big challenges on integrating transportation project data from distinct sources. Differences in meaning of data elements may lead to miscom...

A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports.

Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92–99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.

Text-Mining, Structured Queries, and Knowledge Management on Web Document Corpora

Wikipedia's InfoBoxes play a crucial role in advanced applications and provide the main knowledge source for DBpedia and the powerful structured queries it supports. However, InfoBoxes, which were created by crowdsourcing for human rather than computer consumption, suffer from incompleteness, inconsistencies, and inaccuracies. To overcome these problems, we have developed (i) the IBminer system that extracts InfoBox information by text-mining Wikipedia pages, (ii) the IKBStore system that integrates the information derived by IBminer with that of DBpedia, YAGO2,WikiData,WordNet, and other sources, and (iii) SWiPE and InfoBox Editor (IBE) that provide a user-friendly interfaces for querying and revising the knowledge base. Thus, IBminer uses a deep NLP-based approach to extract from text a semantic representation structure called TextGraph from which the system detects patterns and derives subject-attribute-value relations, as well as domain-specific synonyms for the knowledge base. IKBStore and IBE complement the powerful, user-friendly, by-example structured queries of SWiPE by supporting the validation and provenance history for the information contained in the knowledge base, along with the ability of upgrading its knowledge when this is found incomplete, incorrect, or outdated.

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

BackgroundBacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field.MethodsWe present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors.ResultsWe participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points.ConclusionsWe have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.

A Region-based Approach to the Automated Marking of Short Textual Answers


 
 
 
 Automated marking of short textual answers is a challenging task due to the difficulties involved in accurately “understanding” natural language text. However, certain purpose-built Natural Language Processing (NLP) techniques can be used for this purpose. This paper describes an NLP-based approach to automated assessment that extends an earlier approach [1] to enable the automated marking of longer answers as well as answers that are partially correct. In the extended approach, the original Question Answer Language (QAL) is augmented to support the definition of regions of text that are expected to appear in a student’s answer. In order to explain the extensions to QAL, we present worked examples based on real exam questions. The system’s ability to accurately mark longer answer texts is shown to be on a par with that of existing state-of-the-art short-answer marking systems which are not capable of marking such longer texts.
 
 
 
 

A Region-based Approach to the Automated Marking of Short Textual Answers


 
 
 
 Automated marking of short textual answers is a challenging task due to the difficulties involved in accurately “understanding” natural language text. However, certain purpose-built Natural Language Processing (NLP) techniques can be used for this purpose. This paper describes an NLP-based approach to automated assessment that extends an earlier approach [1] to enable the automated marking of longer answers as well as answers that are partially correct. In the extended approach, the original Question Answer Language (QAL) is augmented to support the definition of regions of text that are expected to appear in a student’s answer. In order to explain the extensions to QAL, we present worked examples based on real exam questions. The system’s ability to accurately mark longer answer texts is shown to be on a par with that of existing state-of-the-art short-answer marking systems which are not capable of marking such longer texts.
 
 
 
 

Mapping Partners Master Drug Dictionary to RxNorm using an NLP-based approach

ObjectiveTo develop an automated method based on natural language processing (NLP) to facilitate the creation and maintenance of a mapping between RxNorm and a local medication terminology for interoperability and meaningful use purposes. MethodsWe mapped 5961 terms from Partners Master Drug Dictionary (MDD) and 99 of the top prescribed medications to RxNorm. The mapping was conducted at both term and concept levels using an NLP tool, called MTERMS, followed by a manual review conducted by domain experts who created a gold standard mapping. The gold standard was used to assess the overall mapping between MDD and RxNorm and evaluate the performance of MTERMS. ResultsOverall, 74.7% of MDD terms and 82.8% of the top 99 terms had an exact semantic match to RxNorm. Compared to the gold standard, MTERMS achieved a precision of 99.8% and a recall of 73.9% when mapping all MDD terms, and a precision of 100% and a recall of 72.6% when mapping the top prescribed medications. ConclusionThe challenges and gaps in mapping MDD to RxNorm are mainly due to unique user or application requirements for representing drug concepts and the different modeling approaches inherent in the two terminologies. An automated approach based on NLP followed by human expert review is an efficient and feasible way for conducting dynamic mapping.

Identifying optimal cleaning cycles for heat exchangers subject to fouling and ageing

Fouling of heat exchangers causes reduced heat transfer and other penalties. Regular cleaning represents one widely used fouling mitigation strategy, where the schedule of cleaning actions can be optimised to minimise the cost of fouling. This paper investigates, for the first time, the situation where there are two cleaning methods available so that the mode of cleaning has to be selected as well as the cleaning interval. Ageing is assumed to convert the initial deposit, labelled ‘gel’, into a harder and more conductive form, labelled ‘coke’, which cannot be removed by one of the cleaning methods. The second method can remove both the gel layer and the coke layer, but costs more and requires the unit to be off-line longer for cleaning. Experimental data demonstrating the effects of ageing are presented. The industrial application is the comparison of cleaning-in-place methods with off-line mechanical cleaning. A process model is constructed for an isolated counter-current heat exchanger subject to fouling, where ageing is described by a simple two-layer model. Solutions generated by an NLP-based approach prove to be superior to a simpler heuristic. A series of case studies demonstrate that combinations of chemical and mechanical cleaning can be superior to mechanical cleaning alone for certain combinations of parameters.

Parametric Sensitivity Analysis of Perturbed PDE Optimal Control Problems with State and Control Constraints

We study parametric optimal control problems governed by a system of time-dependent partial differential equations (PDE) and subject to additional control and state constraints. An approach is presented to compute the optimal control functions and the so-called sensitivity differentials of the optimal solution with respect to perturbations. This information plays an important role in the analysis of optimal solutions as well as in real-time optimal control. The method of lines is used to transform the perturbed PDE system into a large system of ordinary differential equations. A subsequent discretization then transcribes parametric ODE optimal control problems into perturbed nonlinear programming problems (NLP), which can be solved efficiently by SQP methods. Second-order sufficient conditions can be checked numerically and we propose to apply an NLP-based approach for the robust computation of the sensitivity differentials of the optimal solutions with respect to the perturbation parameters. The numerical method is illustrated by the optimal control and sensitivity analysis of the Burgers equation.