Articles tagged with Natural Language Processing
A Penn State research team proposes a new information-filtering approach to predict future health information needs of online community participants. The approach incorporates user profiles, past posts and replies to categorize online content and provide more personalized healthcare resources.
A new study developed a natural language processing system to extract social risk factors, such as financial instability and housing insecurity, from clinical notes. The system showed excellent performance when ported to a new health system and tested on over six million clinical notes.
A new study shows that a rule-based natural language processing tool successfully identified patients with unstable access to transportation, food insecurity, social isolation, financial problems, and signs of abuse or exploitation. The tool performed better than deep learning algorithms in identifying these social determinants of health.
Researchers in Africa have developed a roadmap to create better AI-driven tools for African languages, addressing the limited availability of language data. The study identifies key players and themes to consider in designing effective language tools, including writers, linguists, software engineers, and entrepreneurs.
Researchers used Moral Foundations Theory to analyze hateful language in Nazi propaganda, social media posts, and large text corpora. They found that hate speech often appeals to idealized values such as purity and loyalty.
A new study applies natural language processing to extract information on housing challenges, financial stability, and employment status from unstructured patient data. The researchers developed three algorithms to successfully identify social determinants of health, which can help clinicians and healthcare systems provide better care.
Researchers developed a mobile application to detect Alzheimer's and mild cognitive impairment from speech data. The app achieved high accuracy rates, demonstrating its potential as an early detection tool.
Researchers used AI to analyze speech patterns of patients with Parkinson's disease, finding they spoke in shorter sentences with more verbs and fewer common nouns. The study suggests potential early detection methods for the condition through conversational analysis.
Researchers found that using positive trigger words can retrain large language models and result in less biased responses. The team analyzed GPT-2's responses to user prompts about different countries worldwide and found a significant impact on the types of adjectives used to describe citizens.
Researchers at the University of São Paulo used artificial intelligence and Twitter to develop a database and models that can detect depression and anxiety before clinical diagnosis. The study found that BERT performed best in predicting depression and anxiety, with a statistically significant difference from LogReg.
Researchers found that syntactic surprise, a measure of unexpected word arrangement, significantly improves marketing message efficacy. The optimal level of syntactic surprise falls within the middle range, with messages being most effective at a medium level.
A new study explores how language shapes attention and engagement, finding that easier-to-process language and emotional triggers like anxiety, excitement, and hope encourage readers to persist. Language, meanwhile, can discourage readers from continuing to read.
Researchers have created a transformer model for Metal-Organic Frameworks (MOFs), allowing for faster results and less data. The MOFTransformer model predicts key properties such as hydrogen storage capacity with improved accuracy.
A study analyzing over 800,000 tweets found that negative emotions expressed about geoengineering can easily fall into conspiracy theories. The researchers also discovered a strong link between geoengineering and the chemtrail conspiracy theory, with toxic tweets spilling over across regional boundaries.
Researchers at Cornell University developed an AI tool to track online debates and detect when tensions are escalating. ConvoWizard provides real-time feedback to users on potentially incendiary language, encouraging constructive debate.
A new study analyzed nearly 20,000 conversations between online users and a chatbot called SimSimi to understand user interactions during the COVID-19 pandemic. The researchers found that daily chats with the chatbot mostly led to positive emotions and reduced negative moods during lockdowns.
Researchers found that providing language descriptions of tools can accelerate a simulated robotic arm's learning of tool manipulation. The team used GPT-3 to obtain tool descriptions and showed improved performance in tasks such as pushing, lifting, sweeping, and hammering with new tools.
A digital detection of dementia study aims to evaluate the practical use of an AI tool for early identification of Alzheimer's disease and related dementias. The study uses a passive digital marker and patient-reported outcomes survey, which have shown potential in detecting mild cognitive impairment and reducing healthcare costs.
Researchers have identified a method that can automatically detect doxing on Twitter with high accuracy, which could help protect users from cyberbullying. The approach uses machine learning to differentiate between self-disclosures and malicious disclosures of sensitive personal information.
A new $1.328M grant will help assess and improve the reporting quality in randomized clinical trial protocols and result publications using NLP methods. The goal is to promote high reporting standards, synthesizing evidence, and open science practices throughout the scientific ecosystem.
Researchers found that AI language models exhibit significant implicit bias against individuals with disabilities, resulting in inaccurate sentiment analysis and potential misclassification of posts as toxic. The models' biases were apparent in various applications, including autocorrect and social media content moderation.
Researchers developed an AI-based screening method that models drug and target protein interactions using natural language processing techniques. The technique achieved high accuracy in identifying promising drug candidates, which can accelerate the exploration of new medicines and repurpose existing ones.
Researchers developed an AI tool using natural language processing and machine learning to identify people who inject drugs in electronic health records. The model accurately identified PWIDs in 1,000 records from 2003-2014, significantly improving clinical decision making and resource allocation.
A new project aims to develop resources and AI models to assess biomedical publications for citation accuracy and integrity, benefiting researchers, journals, and policymakers. The project, led by Associate Professors Halil Kilicoglu and Jodi Schneider, received a $300,000 grant from the US Department of Health and Human Services.
A Cornell-led group of researchers has developed a search method that identifies terms similar to cancer screening tests in colloquial language, expanding conventional data-collection pipelines. The technique was applied to YouTube searches, retrieving relevant videos using words semantically close to the main term.
Researchers found that increasing active listening and empathy in a firm's response can evoke customer gratitude, even if the actual failure is not yet recovered. The study shows that these strategies can be easy-to-implement implications for companies handling complaints via social media.
Researchers used natural language processing and machine learning to analyze nearly 35,500 death records, identifying the most common substances involved in overdose deaths. The system reduced data processing time by months, allowing for more rapid public health responses and interventions.
A research team led by Prof. Dr. Birte Höcker applied a computer-based natural language processing model to protein research, creating new proteins capable of stable folding and defined functions. The ProtGPT2 model generates proteins with differentiated structures, eliminating the need for functionalization processes.
A retrospective study found an AI algorithm for detecting incidental pulmonary embolus (iPE) on contrast-enhanced chest CT examinations had high NPV and moderate PPV, even identifying iPEs missed by radiologists. The tool's diagnostic performance did not show significant variation across study subgroups.
Researchers at Boston University developed an AI-powered computational model that can detect cognitive impairment from audio recordings of neuropsychological tests. The model was trained on over 1,000 individuals and accurately distinguished between healthy individuals and those with dementia.
A UTSA professor will use a five-year $550,000 grant to study natural language processing and develop NLP models tailored to specific population groups. The goal is to improve the accuracy and relevance of these models in everyday applications.
A UC San Diego study reveals six overarching themes contributing to physician job stress and suicide, including physical health deterioration and financial stress. The research calls for short-term solutions like improved access to primary care and long-term changes to address workplace culture and self-care.
A Concordia study analyzed gender patterns in AI over two decades, finding women are making strides despite underrepresentation. Notably, female-male collaboration has increased, while core positions held by women remain scarce, tied to factors like family obligations and male-dominated environments.
Researchers aim to develop an audio health library to enhance communication during triadic visits. The study will analyze primary care visits and conduct user-design sessions to refine the system.
Researchers used AI technologies to conclude that male characters are four times more prevalent in literature than female characters. The study's findings also showed that the discrepancy between male and female characters decreases under female authorship.
Researchers at UCF create brain-like devices that enable AI to function independently, allowing technology like robots and voice assistants to operate in remote areas or space. The devices use parallelism and in-memory computing, similar to the brain, for AI and unsupervised learning.
A study found that trainee teachers who received AI-generated feedback improved their diagnostic reasoning, identifying potential learning difficulties in pupils more accurately. The AI system analyzed the trainees' work and provided clear, adaptive feedback.
Researchers from UC Davis MIND Institute develop a home-based language testing method using telehealth, allowing Spanish-speaking parents to assess their children's language abilities. The study found the parent-led test to be feasible, reliable, and effective in both English and Spanish-speaking groups.
A new study of online relationship support finds that men tend to experience emotional pain more than women when their relationship takes a turn for the worse. The study analyzed over 184,000 people who posted their relationship problems online and found that communication problems were the #1 most frequent issue, with nearly 1 in 5 pe...
A new study examines vaccination misinformation on social media, revealing that fear-provoking messaging fuels its spread. The research team developed predictive models to identify true and false information on Twitter, categorizing messages as positive or negative cause-effect statements.
A new AI-powered algorithm, GEM, has been developed to quickly identify genetic causes of serious disease in newborns. The technology leverages machine learning and natural language processing to analyze vast amounts of genomic data and clinical records, achieving an accuracy rate of 92% compared to existing tools.
A Mount Sinai study found that polygenic risk scores were no better at predicting worsening symptoms than written reports in schizophrenia patients. The results raise questions about the use of polygenic risk scores in real-world situations, suggesting a doctor's report may be an untapped source of predictive information.
Researchers at Göttingen University found that customers tend to react negatively to chatbots initially, but positive reactions occur when mistakes lead to a deeper understanding of issues. Disclosing chatbot presence can actually improve customer loyalty in cases where errors occur.
Artificial intelligence natural language computer applications hold promise for health care, but their potential and pitfalls need thoughtful exploration. The authors identify realistic applications such as relieving routine tasks and improving customer service, but also emphasize the need for 'serious guardrails' to prevent harm.
Researchers found that direct compensation for posting written reviews increases positive emotion, but may lead to overly glowing reviews that don't accurately reflect the experience. In experiments, incentives increased review positivity by 55%, but had unforeseen negative consequences for consumers.
Researchers create an approach to flexibly interpret and respond to Soldier intent derived from spoken dialogue with autonomous systems. The technology enables bi-directional conversational interactions between Soldiers and autonomous systems, allowing for hands-free operation to improve situational awareness.
A new study from Simon Fraser University found that women's voices are underrepresented in Canadian news media, with men quoted three times more often than women. Despite having prominent female leaders, the research team identified key areas for improvement, including hiring more women as reporters.
A study analyzing Reddit posts found that users' language changed three months before a breakup, becoming more personal and informal. This change persisted for six months after the split, while some users lingered in online communities, rehashing their breakup story.
A breakthrough in optical neural networks accelerates computing speed and processing power to over 1000 times that of previous processors. The system can process record-sized images and achieve full facial image recognition.
Researchers have discovered subnetworks within BERT that can complete the same task more efficiently, reducing computing costs and increasing accessibility to state-of-the-art natural language processing. The 'lottery ticket hypothesis' identifies these leaner subnetworks, which can be repurposed for multiple tasks.
A new study examines the 'word-of-machine' effect, where consumer preference for AI recommenders is influenced by the importance of utilitarian versus hedonic attributes. When utilitarian features are emphasized, consumers prefer AI over human assistance, while hedonic features lead to a preference for humans.
A new study uses artificial intelligence technologies to analyze natural language patterns to discern degrees of loneliness in older adults. The study found that NLP-based tools could help reconcile the differences between research assessments and an individual's subjective experience of loneliness, improving how clinicians and familie...
A joint laboratory is being developed by George Mason University and Dartmouth researchers to extract knowledge bases from technical documents. The U.S. Department of the Navy has allocated $529,194 for this project, which aims to create a distributed computing system to achieve large-scale document generation.
Researchers developed a recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems, exploiting the advantages of low-precision, noisy, and variable neurons. This work aims to enable compact and efficient real-time processing for applications such as bio-signal processing and brain-machine interfaces.
Regenstrief Institute's Eneida Mendonca and Umberto Tachinardi will share their expertise on artificial intelligence and data infrastructure at a Brazilian workshop. They discuss AI in natural language processing and building data infrastructure for precision medicine.
Researchers at Carnegie Mellon University have created a computer model that can translate text describing physical movements into simple computer-generated animations. The Joint Language-to-Pose model enables sentences and physical motions to be jointly embedded, learning how language is related to action, gestures, and movement.
Studies in linguistics and experimental psychology demonstrate that our minds can assign linguistic structure to non-linguistic content on the fly. Gestures and visual animations help researchers understand how inferences are generated, suggesting that all inferential types result from general, productive processes.
Researchers used AI to analyze electronic health records and identified 16 medical concepts predicting alcohol misuse in 78% of cases. This method could overcome staffing and patient barriers for screening and referral programs at trauma centers.
A team of researchers identified an algorithm called 'nearest-neighbor chaining' that describes how word senses accumulate over time. The study analyzed 1,000 years of English development and found that new word senses emerge through an efficient mechanism that expresses new ideas via a compact set of words.
A new KIT Motion-Language Dataset has been created to support the development of robot activities based on natural language input. The dataset, which includes over 4,000 motions and 6,200 annotations in natural language, aims to unify and standardize research linking human motion and natural language.