Terms Of Frequency Research Articles

인천 관광지의 속성별 평가를 위한 딥러닝 기반 감성분석

This study applied the IPA(importance-performance analysis) methodology to unstructured text data to evaluate tourists' satisfaction levels with Incheon attractions. Focusing on attractions within the maintenance and improvement-needed areas identified by IPA, it extracted key positive and negative keywords and sentences directly from tourists' opinions. By applying a text-based approach to evolving IPA research, this exploratory study assessed the concrete satisfaction and dissatisfaction elements felt by tourists. The study limited its scope to 15 Incheon attractions and analyzed 114,007 sentences from Naver blogs written by tourists. Satisfaction levels were derived through deep learning sentiment analysis of the text, while the importance of each attraction was determined by the TF-IDF(term frequency-inverse document frequency) ratio of the attraction names in the text categorized by attributes such as attraction, transportation, activities, and food/drink. Key positive and negative keywords and sentences were extracted using the TextRank algorithm, focusing on attractions in the maintenance and improvementneeded areas. The findings reveal that experiential elements of attractions act as differentiating factors, while their absence can provoke negative reactions from tourists. Moreover, content reflecting local culture and history, the development of family-friendly experiential attractions, parking space expansion, and food service improvements emerged as crucial elements for attraction development.

Evolution of research related to how a sedentary lifestyle influences the aging process: a bibliometric review

Abstract Aim The world’s population suffers greatly from sedentary lifestyles, which are becoming more common as a result of factors such as a lack of easily accessible places to exercise, a rise in sedentary work practices such as office work, and the pervasive use of television and video devices. Consequently, associated health problems are escalating. The primary goal of this study was to examine the state and features of current trends in international publications regarding the effects of sedentary behavior on the elderly. Subject and methods A bibliometric analysis spanning the years 1994–2022, identifying a total of 311 documents, was carried out using the Web of Science Core Collection. Results The findings showed an exponential increase of 79% from 1997 to the present. The most productive journal categories, authors, and countries were Geriatrics & Gerontology and Public, Environmental & Occupational Health; authors S. Amagasa, N. Fukushima, S. Inoue, and H. Kikuchi; and the United States, respectively. Considerable variation was found in the keywords; “aging” and “sedentary lifestyle” were the most frequent terms, with 97 and 37 instances, respectively. Conclusion Maximum investment in programs promoting physical activity is necessary to improve quality of life as people age. Participants’ mental health will improve as a result of these programs, and their risk of illness will decrease.

The Roles of Rule Type and Word Term in the Deductive Reasoning of Adults with and without Dyslexia.

Despite its importance to everyday functioning, reasoning is underexplored in developmental dyslexia. The current study investigated verbal deductive reasoning on the Wason selection task, not previously used in dyslexia research despite its well-established pedigree. Reasoning rule was manipulated, with the conditional rules varying in the logical values presented. The word frequency and imageability of the word terms was also manipulated. Twenty-six adults with dyslexia and 31 adults without dyslexia completed Wason selection task problems. No group difference in reasoning accuracy or completion time was found. However, the participants were most accurate when reasoning with the rule type "If p, then not q" and least accurate with the rule type "If p then q". More trials were also answered correctly when the word terms were highly imageable but of average word frequency. These findings are in line with the general reasoning literature. Dyslexia status did not interact with either rule type or word term type. The study expands upon previous research by testing verbal deductive reasoning in dyslexia, highlighting the role of imageability in facilitating reasoning performance for all, regardless of the presence or absence of dyslexia. Implications for the design of educational materials are considered.

On improving conversational interfaces in educational systems

Conversational Intelligent Tutoring Systems (CITS) have drawn increasing interest in education because of their capacity to tailor learning experiences, improve user engagement, and contribute to the effective transfer of knowledge. Conversational agents employ advanced natural language techniques to engage in a convincing human-like tutorial conversation. In solving math word problems, a significant challenge arises in enabling the system to understand user utterances and accurately map extracted entities to the essential problem quantities required for problem-solving, despite the inherent ambiguity of human natural language. In this study, we propose two possible approaches to enhance the performance of a particular CITS designed to teach learners to solve arithmetic–algebraic word problems. Firstly, we propose an ensemble approach to intent classification and entity extraction, which combines the predictions made by two distinct individual models that use constraints defined by human experts. This approach leverages the intertwined nature of the intents and entities to yield a comprehensive understanding of the user’s utterance, ultimately aiming to enhance semantic accuracy. Secondly, we introduce an adapted Term Frequency-Inverse Document Frequency technique to associate entities with problem quantity descriptions. The evaluation was conducted on the AWPS and MATH-HINTS datasets, containing conversational data and a collection of arithmetical and algebraic math problems, respectively. The results demonstrate that the proposed ensemble approach outperforms individual models, and the proposed method for entity–quantity matching surpasses the performance of typical text semantic embedding models.

Deep learning approach to detect cyberbullying on twitter

AbstractIn recent years, especially children and adolescents have shown increased interest in social media, making them a potential risk group for cyberbullying. Cyberbullying posts spread very quickly, often taking a long time to be deleted and sometimes remaining online indefinitely. Cyberbullying can have severe mental, psychological, and emotional effects on children and adolescents, and in extreme cases, it can lead to suicide. Turkey is among the top 10 countries with the highest number of children who are victims of cyberbullying. However, there are very few studies conducted in the Turkish language on this topic. This study aims to identify cyberbullying in Turkish Twitter posts. The Multi-Layer Detection (MLP) based model was evaluated using a dataset of 5000 tweets. The model was trained using both social media features and textual features extracted from the dataset. Textual features were obtained using various feature extraction methods such as Bag of Words (BOW), Term Frequency-Inverse Term Frequency (TF-IDF), Hashing Vectorizer, N-gram, and word embedding. These features were utilized in training the model, and their effectiveness was evaluated. The experiments revealed that the features obtained from TF-IDF and unigram methods significantly improved the model’s performance. Subsequently, unnecessary features were eliminated using the Chi-Square feature selection method. The proposed model achieved a higher accuracy of 93.2% compared to machine learning (ML) methods used in previous studies on the same dataset. Additionally, the proposed model was compared with popular deep learning models in the literature, such as LSTM, BLSTM, and CNN, demonstrating promising results.

Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms

Background and objectivesIn the last decade, there has been a growing interest in applying artificial intelligence (AI) systems to breast cancer assessment, including breast density evaluation. However, few models have been developed to integrate textual mammographic reports and mammographic images. Our aims are (1) to generate a natural language processing (NLP)-based AI system, (2) to evaluate an external image-based software, and (3) to develop a multimodal system, using the late fusion approach, by integrating image and text inferences for the automatic classification of breast density according to the American College of Radiology (ACR) guidelines in mammograms and radiological reports. MethodsWe first compared different NLP models, three based on n-gram term frequency – inverse document frequency and two transformer-based architectures, using 1533 unstructured mammogram reports as a training set and 303 reports as a test set. Subsequently, we evaluated an external image-based software using 303 mammogram images. Finally, we assessed our multimodal system taking into account both text and mammogram images. ResultsOur best NLP model achieved 88 % accuracy, while the external software and the multimodal system achieved 75 % and 80 % accuracy, respectively, in classifying ACR breast densities. ConclusionAlthough our multimodal system outperforms the image-based tool, it currently does not improve the results offered by the NLP model for ACR breast density classification. Nevertheless, the promising results observed here open the possibility to more comprehensive studies regarding the utilization of multimodal tools in the assessment of breast density.

A novel optimization-assisted multi-scale and dilated adaptive hybrid deep learning network with feature fusion for event detection from social media

ABSTRACT Social media networks become an active communication medium for connecting people and delivering new messages. Social media can perform as the primary channel, where the globalized events or instances can be explored. Earlier models are facing the pitfall of noticing the temporal and spatial resolution for enhancing the efficacy. Therefore, in this proposed model, a new event detection approach from social media data is presented. Firstly, the essential data is collected and undergone for pre-processing stage. Further, the Bidirectional Encoder Representations from Transformers (BERT) and Term Frequency Inverse Document Frequency (TF-IDF) are employed for extracting features. Subsequently, the two resultant features are given to the multi-scale and dilated layer present in the detection network of GRU and Res-Bi-LSTM, named as Multi-scale and Dilated Adaptive Hybrid Deep Learning (MDA-HDL) for event detection. Moreover, the MDA-HDL network’s parameters are tuned by Improved Gannet Optimization Algorithm (IGOA) to enhance the performance. Finally, the execution of the system is done over the Python platform, where the system is validated and compared with baseline methodologies. The accuracy findings of model acquire as 94.96 for dataset 1 and 96.42 for dataset 2. Hence, the recommended model outperforms with the superior results while detecting the social events.

A semantic-based model with a hybrid feature engineering process for accurate spam detection

Detecting spam emails is essential to maintaining the security and integrity of email communication. Existing research has made significant progress in developing effective spam detection models, but challenges remain in improving classification performance and adaptability to evolving spamming techniques. In this study, we propose a novel spam detection model with a comprehensive feature engineering approach that combines term frequency-inverse document frequency (TF-IDF) vectorizer and word embedding features to optimize the feature space. Our contribution lies in integrating semantic-based word embeddings, leveraging pre-existing knowledge to capture the semantic meaning of words and enhance the representation of email texts. To identify the most suitable word embedding technique for our model, we evaluated GloVe, Word2Vec, and FastText. GloVe was selected for its better performance, which is the result of its pre-training on a large and diverse text corpus. Furthermore, the model was evaluated without word embeddings, which did not exhibit the same effectiveness level as our word embedding-based model. Additionally, we utilized the support vector machine as a classifier and hyperparameter tuning technique to identify our model’s most effective parameter values. The proposed model was tested on two datasets. The experimental results showed that our model outperformed the other models discussed in the literature, achieving an accuracy of 99.5% on the SpamAssassin dataset, and 99.28% on the Enron-Spam dataset.

Comparative Analysis on Automatic Keyphrase Extraction (AKPE) Techniques

Abstract— Extracting Keyphrase from the large corpus manually is a tedious task. To overcome this challenging task Automatic Keyphrase Extraction (AKPE) techniques have been used for Keyphrase extraction. Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrase constitutes a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering, and classification. This paper contains a brief description of automatic Keyphrase extraction techniques which consist of various algorithms and also a comparative study on automotive Keyphrase extraction techniques such as KEA, TF-IFD, RAKE, TextRank. Keywords— Keyphrase Extraction, Techniques, Comparison, Keyphrase Extraction Algorithm, Term Frequency- Inverse Frequency Document, Rapid Automatic Keyphrase Extraction, TextRank.

Enhancing Word Sense Disambiguation for Amharic homophone words using Bidirectional Long Short-Term Memory network

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fidel's ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (both pronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, which tackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m'hur) and ምሑር(m'hur), በአል(be'al) and በዢል(be'al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), decision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limited datasets.

HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social media.

Social media platforms serve as communication tools where users freely share information regardless of its accuracy. Propaganda on these platforms refers to the dissemination of biased or deceptive information aimed at influencing public opinion, encompassing various forms such as political campaigns, fake news, and conspiracy theories. This study introduces a Hybrid Feature Engineering Approach for Propaganda Identification (HAPI), designed to detect propaganda in text-based content like news articles and social media posts. HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). Hybrid feature engineering entails the amalgamation of various features, including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), Sentimental features, and tweet length, among others. Multiple Machine Learning classifiers undergo training and evaluation utilizing the proposed methodology, leveraging a selection of 40 pertinent features identified through the hybrid feature selection technique. All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. The SVM-based HaPi (SVM-HaPi) exhibits superior performance among traditional algorithms, achieving precision, recall, F-Measure, and overall accuracy of 0.69, 0.69, 0.69, and 69.2%, respectively. Furthermore, the proposed approach is compared to well-known existing approaches where it overperformed most of the studies on several evaluation metrics. This research contributes to the development of a comprehensive system tailored for propaganda identification in textual content. Nonetheless, the purview of propaganda detection transcends textual data alone. Deep learning algorithms like Artificial Neural Networks (ANN) offer the capability to manage multimodal data, incorporating text, images, audio, and video, thereby considering not only the content itself but also its presentation and contextual nuances during dissemination.

Analysis and mapping of harm reduction research in the context of injectable drug use: identifying research hotspots, gaps and future directions

BackgroundHarm reduction is a crucial approach in addressing the multifaceted challenges of injectable drug use. This paper presents an analysis and mapping of the existing literature on harm reduction research in the context of injectable drug use. By reviewing a comprehensive set of scholarly articles, this study identifies research hotspots, knowledge gaps, and future directions in the field. The findings provide valuable insights for researchers, policymakers, and practitioners to guide future research efforts and inform evidence-based harm reduction interventions.MethodsData for the study was obtained from the Scopus database, using keywords and phrases related to harm reduction and injectable drug use. Validation methods were employed to verify the accuracy and comprehensiveness of the search strategy. Data analysis involved identifying growth patterns, key contributors, mapping frequent terms, identifying research hotspots, and identifying emerging research directions.ResultsA total of 971 articles were found, with a notable increase from 2015 to 2022. The International Journal of Drug Policy (n = 172, 17.7%) and the Harm Reduction Journal (n = 104, 10.7%) were the most prolific journals, and the United States (n = 558, 57.5%) had the highest number of publications. The Johns Hopkins University (n = 80, 8.5%) was the most prolific institution. Mapping of frequent author keywords revealed the main keywords, including harm reduction, HIV, hepatitis C, and opioid overdose. The highly cited articles cover a broad time span and focus on topics like naloxone distribution, HIV and hepatitis C transmission, while recent articles concentrate on emerging issues such as the impact of the COVID-19 pandemic, fentanyl-related concerns, stigma reduction, and needle and syringe programs. Both sets of articles share a common focus on harm reduction strategies, but recent publications highlight current challenges and developments in the field.ConclusionsThis study provides insights into research landscape on harm reduction in injectable drug use. Research is concentrated in high-income countries, emphasizing the need for more research in low- and middle-income countries. Recent publications focus on emerging challenges like COVID-19 and fentanyl. Research gaps highlight the need for studies in diverse populations, social determinants, program evaluation, and implementation strategies to enhance harm reduction interventions.

DÉDUCTION EFFICACE DE LANGUE OFFENSIVE UTILISANT L'APPRENTISSAGE PROFONDE DANS LES MÉDIAS SOCIAUX

Offensive language detection is the technique of identifying and detecting user-generated offensive comments such as insults, pain, profanity, and racism that are targeted at a specific individual or group on social media. As social media platforms become more prominent, offensive language is used more frequently, becoming a major challenge in modern society. A novel effective offensive language classification (EOLC) technique has been proposed to overcome these challenges. English language tweets from YouTube and X (Twitter) with offensive, mild, swear, and non-offensive tweets are used in this paper. Initially, the tweets and comments are pre-processed, and the features are extracted using different techniques, namely term frequency-inverse document frequency (TF-IDF), WordVec, and lexicon-based features. The extracted features are classified using the graph-based deep learning (GDL) method for numerical representation and decision-making. GDL network is optimized with red fox optimization (RFO) to normalize the weight and biases of the network and achieve better accuracy. The proposed GDL model achieves the highest levels of classification accuracy on the X (Twitter) and YouTube datasets, with 95.5 % and 96.8 %, respectively. The results obtained from GDL are more accurate and of higher quality than those obtained from traditional classifiers. The proposed EOLC method improves the overall accuracy by 5.56 %, 7.4 %, 7.7 %, and 10.2 % better than Text CNN, CNN-LSTM, DRNN, and LogitBoost, respectively.

Artificial Intelligence or Nursing Student? Revisiting Clues in the Connectives.

Recent research at a single-purpose nursing institution has suggested a means to authenticate student writing by distinguishing it from artificial intelligence (AI)-generated text through the detection of key terms. The purpose was to replicate and expand the research that identified key terms present in student writing but absent from AI-generated text. A total of 5 generative AI writing tools were fed prompts to collect 14 787 words. Using the Search function on word processing software, the frequency of the terms, because, since, so, then, thing, think, and too, was measured and compared against earlier published findings from AI and students. The replication study was successful for the terms since, then, thing, think, and too. Measuring key term frequency may be a path to authenticate student writing. While no tool can provide certainty of original authorship, the absence of key terms in a student submission may suggest AI authorship.

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

Detecting review fraud using metaheuristic graph neural networks

Although online reviews play a critical role in shaping consumer behavior and establishing institutional trust, this reliance on online reviews has given rise to a proliferation of fake reviews. Consequently, there is an urgent need for the development of robust mechanisms to identify and mitigate these inauthentic reviews. In this study, we introduce an innovative approach for the detection of fake reviews, leveraging a metaheuristic graph neural network model. Our methodology begins by extracting distinctive features from review text, comprising of term frequency—inverse document frequency (TF-IDF) vectors and bidirectional encoder representation transformer (BERT) embedding vectors. We feed these features into a graph convolutional network (GCN) that uses message passing to label each review. Furthermore, we address the optimization of our graph neural network models using Harris Hawk and Cat Swarm optimization techniques. Our experiments reveal that optimization of the GNN with Harris Hawk Optimization yields a significantly enhanced model performance, further validating the efficacy of our detection framework. This research contributes to the ongoing efforts in the realm of online review authenticity and showcases the potential of advanced techniques to combat the proliferation of fraudulent reviews.

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations

ObjectiveThe study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from...

Scientific paper recommender system using deep learning and link prediction in citation network

Today, the number of published scientific articles is increasing day by day, and this has made the process of searching for articles more difficult. The need to provide specific recommender systems (RSs) for suggesting scientific articles is strongly felt in this situation. Because searching for articles based only on matching the titles or content of other articles is not an efficient process. In this research, the combination of two content analysis and citation network is used to design an RS for scientific articles (RECSA). In RECSA, natural language processing and deep learning techniques are used to process the titles and extract the content attributes of the articles. For this purpose, first, the titles of the articles are pre-processed, and by using the Term Frequency Inverse Document Frequency (TF-IDF) criterion, the importance of each word in the title is estimated. Then the dimensions of the obtained attributes are reduced by using a convolutional neural network (CNN). Then, by using the cosine similarity criterion, the content similarity matrix of the articles is calculated based on the attribute vectors. Also, the link prediction approach is used to analyze the connections of scientific articles' citation network. Finally, in the third step of RECSA, the two similarity matrices calculated in the previous steps are combined using an influence coefficient parameter to obtain the final similarity matrix, and the recommendation operation is based on the highest similarity value. The efficiency of RECSA has been evaluated from different aspects and the results have been compared with previous works. According to the results, utilizing the combination of TF-IDF and CNN for analyzing content-based features, leads to at least 0.32 % improvement in terms of precision compared to previous works. Also, by integrating citation and content-based data, the precision of first suggestion in RECSA would be 99.01 % which indicates the minimum improvement of 0.9 % compared to compared methods. The results show that by using RECSA, the recommendation can be done with higher accuracy and efficiency.

Natural Language Processing (NLP) Accurately Identifies LTCF Exposure from Clinical Notes: A Proof-of-Principle Study

Background: Residence or recent stay in a long-term care facility (LTCF) is one of the most important risk factors for multidrug-resistant organism (MDRO) carriage and infection, making reliable identification of LTCF-exposed inpatients a critical priority for infection control day-to-day practice and research. However, because most hospital electronic health records (EHRs) do not include a dedicated field for documenting LTCF exposure, absent manual review of patient charts, identifying LTCF-exposed inpatients is challenging. We aimed to develop an automated, natural language processing (NLP)-based classifier for identifying LTCF exposure from clinical notes. Methods: We randomly sampled 1020 adult admissions from 2016-2021 across the 12-hospital University of Maryland Medical System and manually reviewed each admission’s history & physical (H&P) note for mention of LTCF exposure (Figure 1). After H&P pre-processing, we calculated feature representations for documents based on term frequencies and visually explored between-group (LTCF-exposed vs. LTCF-unexposed) feature differences. To predict LTCF status from the H&P notes, we trained and tuned a LASSO regression-based classifier on 70% of the data with 3-fold cross-validation and 1:1 up-sampling to address class imbalance. The final classifier was evaluated on the 30% held-out sample (not up-sampled), with calculation of the C-statistic (area-under-the-curve, AUC) with bootstrapped 95% confidence intervals, and construction of receiver-operating-characteristic and variable importance plots (R Version 4.3.2). Results: 7% (n=76 cases) of H&P notes documented LTCF exposure. In our visual analysis, the H&P words and phrases that were over-represented among LTCF patients had high face validity (Figure 2). The final LASSO-regression-based classifier achieved a C-statistic of 0.89 (95% CI: 0.80–0.98) on the held-out data for identifying LTCF exposure from the H&P notes (Figure 3). The most important model predictors (i.e., words) for distinguishing LTCF-exposed from LTCF-unexposed patients are reflected in Figure 4. The most important predictor-words of LTCF-exposure were “rehab,” “place,” “status,” “egd,” and “dementia.” Conclusion: In this multi-center study, even a simple NLP classifier demonstrated very strong discrimination for identifying LTCF exposure status from H&P notes, which could substantially reduce the manual review time required to identify LTCF-exposed inpatients. If automated in the electronic health record, it could also inform real-time MDRO screening decisions. Future research is planned to build more sophisticated classifiers using machine learning best practices, to build classifiers for additional MDRO risk factors, and to externally validate NLP classifiers on notes from an external healthcare system.

Keystrokes: A practical exploration of semantic drift in timed word association tasks.

This study investigates the phenomena of semantic drift through the lenses of language and situated simulation (LASS) and the word frequency effect (WFE) within a timed word association task. Our primary objectives were to determine whether semantic drift can be identified over the short time (25 seconds) of a free word association task (a predicted corollary of LASS), and whether more frequent terms are generated earlier in the process (as expected due to the WFE). Respondents were provided with five cue words (tree, dog, quality, plastic and love), and asked to write as many associations as they could. We hypothesized that terms generated later in the task (fourth time quartile, the last 19-25 seconds) would be semantically more distant (cosine similarity) from the cue word than those generated earlier (first quartile, the first 1-7 seconds), indicating semantic drift. Additionally, we explored the WFE by hypothesizing that earlier generated words would be more frequent and less diverse. Utilizing a dataset matched with GloVe 300B word embeddings, BERT and WordNet synsets, we analysed semantic distances among 1569 unique term pairs for all cue words across time. Our results supported the presence of semantic drift, with significant evidence of within-participant, semantic drift from the first to fourth time (LASS) and frequency (WFE) quartiles. In terms of the WFE, we observed a notable decrease in the diversity of terms generated earlier in the task, while more unique terms (greater diversity and relative uniqueness) were generated in the 4th time quartile, aligning with our hypothesis that more frequently used words dominate early stages of a word association task. We also found that the size of effects varied substantially across cues, suggesting that some cues might invoke stronger and more idiosyncratic situated simulations. Theoretically, our study contributes to the understanding of LASS and the WFE. It suggests that semantic drift might serve as a scalable indicator of the invocation of language versus simulation systems in LASS and might also be used to explore cognition within word association tasks more generally. The findings also add a temporal and relational dimension to the WFE. Practically, our research highlights the utility of word association tasks in understanding semantic drift and the diffusion of word usage over a sub-minute task, arguably the shortest practically feasible timeframe, offering a scalable method to explore group and individual changes in semantic relationships, whether via the targeted diffusion of influence in a marketing campaign, or seeking to understand differences in cognition more generally. Possible practical uses and opportunities for future research are discussed.