Natural Language Processing Solutions Research Articles

The Algorithmic Gut Feeling – Articulating Journalistic Doxa and Emerging Epistemic Frictions in AI-Driven Data Work

This article explores the epistemic practices and doxa of data workers in a news organisation in Denmark that is currently developing and experimenting with artificial intelligence (AI)-driven recommender systems, machine learning and natural language processing solutions. Previous literature on the changing epistemologies of digital journalism has focused on the increased role of metrics and the transformed practices inside newsrooms, as well as on how journalists perceive and articulate the computational. This article advances these studies by focusing on how data scientists perceive and articulate “the journalistic” when building AI systems for distributing news. Developing the notion of “the algorithmic gut feeling”, the article highlights different frictions present in the articulations of the journalistic doxa in AI-driven data work concerning (1) how to algorithmically define ethics, (2) how to algorithmically categorise and understand relevance, and (3) how to algorithmically curate “a good mix” for the front page. The emerging frictions and algorithmic gut feeling are key to understanding how the doxa of data workers involved and deeply invested in “the good of journalism” at times also transforms journalistic epistemologies of what constitutes “news” and “the right mix” of content in the service of a democratic public.

Revolutionizing Healthcare: NLP, Deep Learning, and WSN Solutions for Managing the COVID-19 Crisis

The COVID-19 outbreak in 2020 catalyzed a global socio-economic upheaval, compelling nations to embrace digital technologies as a means of countering economic downturns and ensuring efficient communication systems. This paper delves into the role of Natural Language Processing (NLP) in harnessing wireless connectivity during the pandemic. The examination assesses how wireless networks have affected various facets of crisis management, including virus tracking, optimizing healthcare, facilitating remote education, and enabling unified communications. Additionally, the article underscores the importance of digital inclusion in mitigating disease outbreaks and reconnecting marginalized communities. To address these challenges, a Dual CNN-based BERT model is proposed. BERT model is used to extract the text features, the internal layers of BERT excel at capturing intricate contextual details concerning words and phrases, rendering them highly valuable as features for a wide array of text analysis tasks. The significance of dual CNN is capturing the unique capability to seamlessly integrate both character-level and word-level information. This fusion of insights from different levels of textual analysis proves especially valuable in handling text data that is noisy, complex, or presents challenges related to misspellings and domain-specific terminology. The proposed model is evaluated using the simulated WSN-based text data for crisis management.

Building lexicon-based sentiment analysis model for low-resource languages

Natural Language Processing (NLP) has transformed machine translation, sentiment analysis, information retrieval, and conversation systems. NLP applications rely on complete linguistic resources, which might be difficult for low-resource languages. NLP solutions for every language require a language-specific dataset. Dataset in a language is essential for NLP solution creation. Over 7000 languages are spoken worldwide. Only around 20 languages have text corpora for NLP applications. English has the most datasets, then Chinese and Spanish. Japanese has several Western European language datasets. For an accurate NLP system, most Asian and African languages lack training datasets. To address this challenge, we propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources. The Hausa language was used as training and evaluation language. The methodology combines lexicon creation; augmentation, annotation, and fine-tuning model, and has been tested on a corpus of Hausa tweets achieving an accuracy of 98 %. The results suggest that our proposed model is a promising tool for sentiment analysis in a variety of applications, such as social media monitoring, customer service, and market research. Our methodology can be used for any low-resource language. The outline of the work done in this paper can be shown as follows:•We propose a methodology for building a lexicon-based sentiment analysis model for languages with limited resources, using the Hausa language as a case study.•The methodology combines lexicon creation, augmentation, annotation, and fine-tuning model, and achieves an accuracy of 98 % on a corpus of Hausa tweets.•The results suggest that the proposed model is a promising tool for sentiment analysis in a variety of applications for low-resource languages.

High Accuracy Location Information Extraction From Social Network Texts Using Natural Language Processing

Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.

The role of social media on the evolution of companies: A Twitter analysis of Streaming Service Providers

In recent years, Social Networks and, in particular, Twitter have proved to be a fertile ground for those scholars and companies interested in exploring the effectiveness of brand marketing communications. This is even more true when it comes to TV Streaming Service Providers, such as Netflix or Amazon. For these types of companies, Twitter represents not only a valuable source of data for business intelligence, but also a connected and co-viewing platform and outage detection system. In this paper, we carry out our analysis by exploring and comparing, through disparate machine learning techniques and natural language processing solutions, the behavior of several Twitter accounts corresponding to different Streaming Service Providers by considering their possible stage in the Technology Adoption Life Cycle. Interestingly, such an analysis allows for the identification of the most suitable strategies that can be carried out on Twitter by Streaming Service Providers to improve the user involvement on the basis of their current stage. To the best of our knowledge, a complete analysis able to depict Twitter strategies of success for Streaming Service Providers does not exist in current literature yet.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Applications of natural language processing in software traceability: A systematic mapping study

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP).Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability.Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.

Clinical Errors From Acronym Use in Electronic Health Record: A Review of NLP-Based Disambiguation Techniques

The adoption of Electronic Health Record (EHR) and other e-health infrastructures over the years has been characterized by an increase in medical errors. This is primarily a result of the widespread usage of medical acronyms and abbreviations with multiple possible senses (i.e., ambiguous acronyms). The advent of Artificial Intelligence (AI) technology, specifically Natural Language Processing (NLP), has presented a promising avenue for tackling the intricate issue of automatic sense resolution of acronyms. Notably, the application of Machine Learning (ML) techniques has proven to be highly effective in the development of systems aimed at this objective, garnering significant attention and interest within the research and industry domains in recent years. The significance of automating the resolution of medical acronym senses cannot be overstated, especially in the context of modern healthcare delivery with the widespread use of EHR. However, it is disheartening to note that comprehensive studies examining the global adoption of EHR, assessing the impact of acronym usage on medical errors within EHR systems, and reporting on the latest trends and advancements in ML-based NLP solutions for disambiguating medical acronyms remain severely limited. In this current study, we present a detailed overview on medical error, its origins, unintended effects, and EHR-related errors as a subclass of clinical error. Furthermore, this paper investigates the adoption of EHR systems in developed and developing nations, as well as the review concludes with an examination of various artificial intelligence techniques, particularly machine learning algorithms for medical acronym and abbreviation disambiguation in EHRs.

Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in thereporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.

TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation.

Colonoscopy plays a critical role in screening of colorectal carcinomas (CC). Unfortunately, the data related to this procedure are stored in disparate documents, colonoscopy, pathology, and radiology reports respectively. The lack of integrated standardized documentation is impeding accurate reporting of quality metrics and clinical and translational research. Natural language processing (NLP) has been used as an alternative to manual data abstraction. Performance of Machine Learning (ML) based NLP solutions is heavily dependent on the accuracy of annotated corpora. Availability of large volume annotated corpora is limited due to data privacy laws and the cost and effort required. In addition, the manual annotation process is error-prone, making the lack of quality annotated corpora the largest bottleneck in deploying ML solutions. The objective of this study is to identify clinical entities critical to colonoscopy quality, and build a high-quality annotated corpus using domain specific taxonomies following standardized annotation guidelines. The annotated corpus can be used to train ML models for a variety of downstream tasks.

Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required—audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.

Digitalizáció és hálózatkutatás

Az elmúlt években fejlődésnek induló digitalizálási módszerek lehetővé tették a digitális bölcsészet területén belül többek között az életrajzi lexikonokat feldolgozó projektek előtérbe kerülését. A biográfiai kutatások célja a már digitalizált lexikonok szövegeinek (pl. Österreichisches Biographisches Lexikon 1815–1950 – a továbbiakban ÖBL – részben strukturált, részben strukturálatlan, szöveges formában tárolt adatainak) átalakítása számítógép által feldolgozható formátumra, illetve az átalakított adatokon hálózati, statisztikai és számítógépes nyelvészeti kutatások elvégzése. Ebben a kontextusban kezdődtek el 2015-ben a kutatások az Osztrák Tudományos Akadémia Új- és Jelenkortörténeti Kutatási Intézetében [Institut für Neuzeit- und Zeitgeschichtsforschung] a Mapping historical networks: Building the new Austrian Prosopographical, Biographical Information System (APIS) projekt keretén belül. A projekt az ÖBL digitális feldolgozását és egy kutatási célokra kifejlesztett számítógépes környezet létrehozását célozza meg, mely a szintén az Osztrák Tudományos Akadémia keretében működő Austrian Centre for Digital Humanities (ACDH) [Osztrák Digitális Bölcsészeti Központ] valamint az Institut für Stadt- und Regionalforschung (ISR) [Város- és Régiókutatási Intézet] együttműködésével valósul meg. Cikkünkben bemutatjuk, hogy az APIS-projekt technikai megvalósítására fejlesztett webalkalmazás milyen technikai és nyelvtechnológiai megoldásokat használ a lexikon számítógépes feldolgozására és az adatok reprezentálására.

Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.

We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient-level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow-up data (0-90 days posttransplant; structured and unstructured for 1-year models; data up to 1 year for 3-year models) on adult solitary kidney transplant recipients transplanted during 2007-2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3-year GL rate was observed among 891 patients (2007-2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61-0.74); Model 3: AUC, 0.72; (95% CI: 0.66-077); Model 4: AUC, 0.84, (95 % CI: 0.79-0.89). One-year GL (AUC, 0.87; Model 4) and 3-year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes.

Introduction to the Special Issue on Computational Anaphora Resolution

Anaphora accounts for cohesion in texts and is a phenomenon under active study in formal and computational linguistics alike. The correct interpretation of anaphora is vital for natural language processing (NLP). For example, anaphora resolution is a key task in natural language interfaces, machine translation, text summarization, information extraction, question answering, and a number of other NLP applications. After considerable initial research, followed by years of relative silence in the early 1980s, anaphora resolution has attracted the attention of many researchers in the last 10 years and a great deal of successful work on the topic has been carried out. Discourseoriented theories and formalisms such as Discourse Representation Theory and Centering Theory inspired new research on the computational treatment of anaphora. The drive toward corpus-based robust NLP solutions further stimulated interest in alternative and/or data-enriched approaches. Last, but not least, application-driven research in areas such as automatic abstracting and information extraction independently highlighted the importance of anaphora and coreference resolution, boosting research in this area. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (Dagan and Itai 1990, 1991; Lappin and Leass 1994; Nasukawa 1994; Kennedy and Boguraev 1996; Williams, Harvey, and Preston 1996; Baldwin 1997; Mitkov 1996, 1998b). The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers, alongside the increasing availability of corpora and other NLP resources (e.g., ontologies). In fact, the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolu