Natural Language Research Articles

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing informatics professor and researchers with ICU experience, developed a mapping table through a four-step process: initially reviewing literature on nurses' concerns, then extracting nursing notes from MIMIC IV and filtering the duplicate notes, subsequently defining and coding these concerns, and finally mapping them according to the CCC. Of 11,430,637 unstructured nursing notes from MIMIC IV, 265 unique notes remained after deduplication, with 208 notes reflecting nurses' concerns and categorized into 15 groups aligned with CCC. This mapping table will be a fundamental tool for predicting clinical deterioration in ICU patients by identifying important lexicons through natural language processing.

AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities

The project "AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)

Representation of Social Determinants of Health terminology in medical subject headings: impact of added terms.

To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH). High priority SDoH terms and definitions were collated from authoritative sources, curated based on publication frequencies, and refined by subject matter experts. Descriptive analyses were used to investigate how PubMed search details and best match results were affected by the addition of SDoH concepts to MeSH. Three information retrieval metrics (Precision, Recall, and F measure) were used to quantitatively assess the accuracy of PubMed search results. Pre- and post-update documents were clustered into topic areas using a Natural Language Processing pipeline, and SDoH relevancy assessed. Addition of 35 SDoH terms to MeSH resulted in more accurate algorithmic translations of search terms and more reliable best match results. The Precision, Recall, and F measures of post-update results were significantly higher than those of pre-update results. The percentage of retrieved publications belonging to SDoH clusters was significantly greater in the post- than pre-update searches. This evaluation confirms that inclusion of new SDoH terms in MeSH can lead to qualitative and quantitative enhancements in PubMed search retrievals. It demonstrates the methodology for and impact of suggesting new terms for MeSH indexing. It provides a foundation for future efforts across behavioral and social science research (BSSR) domains. Improving the representation of BSSR terminology in MeSH can improve PubMed search results, thereby enhancing the ability of investigators and clinicians to build and utilize a cumulative BSSR knowledge base.

Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data

BackgroundPooling data from different sources will advance mental health research by providing larger sample sizes and allowing cross-study comparisons; however, the heterogeneity in how variables are measured across studies poses a challenge to this process.MethodsThis study explored the potential of using natural language processing (NLP) to harmonise different mental health questionnaires by matching individual questions based on their semantic content. Using the Sentence-BERT model, we calculated the semantic similarity (cosine index) between 741 pairs of questions from five questionnaires. Drawing on data from a representative UK sample of adults (N = 2,058), we calculated a Spearman rank correlation for each of the same pairs of items, and then estimated the correlation between the cosine values and Spearman coefficients. We also used network analysis to explore the model’s ability to uncover structures within the data and metadata.ResultsWe found a moderate overall correlation (r = .48, p < .001) between the two indices. In a holdout sample, the cosine scores predicted the real-world correlations with a small degree of error (MAE = 0.05, MedAE = 0.04, RMSE = 0.064) suggesting the utility of NLP in identifying similar items for cross-study data pooling. Our NLP model could detect more complex patterns in our data, however it required manual rules to decide which edges to include in the network.ConclusionsThis research shows that it is possible to quantify the semantic similarity between pairs of questionnaire items from their meta-data, and these similarity indices correlate with how participants would answer the same two items. This highlights the potential of NLP to facilitate cross-study data pooling in mental health research. Nevertheless, researchers are cautioned to verify the psychometric equivalence of matched items.

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.

Translation from Tunisian Dialect to Modern Standard Arabic: Exploring Finite-State Transducers and Sequence-to-Sequence Transformer Approaches

Translation from the mother tongue, including the Tunisian dialect, to modern standard Arabic is a highly significant field in natural language processing due to its wide range of applications and associated benefits. Recently, researchers have shown increased interest in the Tunisian dialect, primarily driven by the massive volume of content generated spontaneously by Tunisians on social media follow-ing the revolution. This paper presents two distinct translators for converting the Tunisian dialect into Modern Standard Arabic. The first translator utilizes a rule-based approach, employing a collection of finite state transducers and a bilingual dictionary derived from the study corpus. On the other hand, the second translator relies on deep learning models, specifically the sequence-to-sequence trans-former model and a parallel corpus. To assess, evaluate, and compare the performance of the two translators, we conducted tests using a parallel corpus comprising 8,599 words. The results achieved by both translators are noteworthy. The translator based on finite state transducers achieved a blue score of 56.65, while the transformer model-based translator achieved a higher score of 66.07.

Vladimir Putin on Channel One, 2000–2022

ABSTRACT Drawing on transcripts from the television network Channel One, a popular news source in Russia, this article addresses the question: “How was Vladimir Putin covered by state-controlled media while the regime became increasingly centralized?” The literature on the subject is scarce and inconclusive. Dictators create different images of themselves, and the portrayals of present-day spin dictators — those who primarily rely on the power of propaganda to persuade rather than dominate — are understudied. While some analysts point to Putin’s omnipresence in mass media, others uncover the lack of media personalization and relatively neutral coverage. Using 385,981 news transcripts from 2000–2022 and relying on techniques from natural language processing, I examine how a present-day autocrat attempts to optimize the intensity of state-controlled propaganda. I uncover three main tendencies. First, during all the years in power, the ruler has been more frequently referred to through positive stories. Second, there is only partial evidence that the relative references to Putin on Channel One have significantly increased over time. Third, during all his years in power, Putin has been more frequently mentioned in domestic news rather than in stories about foreign affairs. However, I also demonstrate that the share of news about foreign affairs and events abroad that mentions the ruler has been increasing every year since 2013. By focusing on the supply side of propaganda, this article contributes to the literature on autocratic resilience and spin dictators.

Systematic Development of an AI Chatbot for Physical Activity Habit Formation in Prehypertension Individual Integrating Needs, Theories, and Evidence.

Prehypertension, an early stage in the development of hypertension, impacts a substantial segment of the adult population worldwide. Addressing this issue, our study introduces HabitBot, an AI-driven chatbot tailored to encourage physical activity (PA) habits among individuals with prehypertension. HabitBot combines natural language processing with multidisciplinary approaches, drawing from both theoretical frameworks and empirical studies. The chatbot development followed a systematic, five-phase process: comprehensive needs assessment, literature review on behavior change theories, analysis for selecting effective behavior change techniques (BCTs), prototype design through intervention mapping, and refining the intervention based on user feedback. The outcome includes a prototype that integrates the Health Action Process Approach and Habit Formation Theory, utilizing twelve identified BCTs effective in fostering PA habits. User feedback further refined the chatbot across multiple dimensions such as user interface, content accessibility, and privacy. HabitBot exemplifies an innovative integration of behavior change strategies with advanced language model technology, paving the way for digital health interventions in chronic disease prevention. Future studies should assess its long-term efficacy in habit formation and explore its applicability to various demographic groups.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls. The resulting annotated data corpus had moderately high interrater reliability (F-score=0.74) and captured a breadth of clinical concepts for extraction with potential utility beyond patient falls. Further research is needed to determine which annotation tasks most benefit from nursing expert annotators, to optimize efficiency when tapping into the invaluable resource represented by the nursing workforce.

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.

A Technological Framework to Support Asthma Patient Adherence Using Pictograms

Background: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health.

A fuzzy-based emotion detection method to classify the attractiveness of urban green spaces

AbstractIn European studies, the most used definition of Urban Green Spaces (UGS) is based on the European Urban Atlas, which includes public green areas primarily used for recreation and green areas adjacent to urban areas that are managed or utilized for recreational purposes. UGS play a vital role in creating sustainable and resilient cities, as they provide essential social benefits for the well-being and health of urban residents. Both planners and scientists acknowledge the importance of involving, actively or passively, citizens in defining criteria for designing and managing inclusive and functional UGS. According to a post-normal science approach, the integration of hard data from scientific sources with soft data gathered from citizens’ engagement holds the potential to shape an innovative support system for public policies addressing significant, urgent, and uncertain challenges pertaining to UGS. Nowadays, the abundance of data generated through online reviews, opinions, and comments allows for collecting valuable information about people’s opinions and sentiments towards UGS. This study proposes a methodological framework that utilizes emotion detection techniques to identify and analyze citizens’ emotions concerning UGS through social reviews. To balance computational costs and classification accuracy, the framework introduces a fuzzy emotion-based classification method called FREDoC (Fuzzy Relevance Emotions Document Classification). This method incorporates a lightweight natural language pro-cessing (NLP) approach to detect and annotate terms associated with specific emotional categories within the text. The framework adopts the psycho-evolutionary classification approach based on R. Plutchik’s observations of general emotional responses. This model is implemented within a Geographical Information System (GIS) for the purpose of categorizing UGS, specifically green parks, according both to WHO report key indicators and to the detected relevant emotions. The outcome is a novel classification model of UGS that can assist decision-makers in identifying the attractiveness of UGS as catalysts for urban transformation processes.

A novel transformer attention‐based approach for sarcasm detection

AbstractSarcasm detection is challenging in natural language processing (NLP) due to its implicit nature, particularly in low‐resource languages. Despite limited linguistic resources, researchers have focused on detecting sarcasm on social media platforms, leading to the development of specialized algorithms and models tailored for Urdu text. Researchers have significantly improved sarcasm detection accuracy by analysing patterns and linguistic cues unique to the language, thereby advancing NLP capabilities in low‐resource languages and facilitating better communication within diverse online communities. This work introduces UrduSarcasmNet, a novel architecture using cascaded group multi‐head attention, which is an innovative deep‐learning approach that employs cascaded group multi‐head attention techniques to enhance effectiveness. By employing a series of attention heads in a cascading manner, our model captures both local and global contexts, facilitating a more comprehensive understanding of the text. Adding a group attention mechanism enables simultaneous consideration of various sub‐topics within the content, thereby enriching the model's effectiveness. The proposed UrduSarcasmNet approach is validated with the Urdu‐sarcastic‐tweets‐dataset (UST) dataset, which has been curated for this purpose. Our experimental results on the UST dataset show that the proposed UrduSarcasmNet framework outperforms the simple‐attention mechanism and other state‐of‐the‐art models. This research significantly enhances natural language processing (NLP) and provides valuable insights for improving sarcasm recognition tools in low‐resource languages like Urdu.

The development of a research intelligence tool for rare disease research in the Netherlands

Abstract This article describes the process of the development of a research intelligence tool to analyse rare disease research in the Netherlands. To the best of our knowledge, this is the first tool that can surface and organise scientific output on rare diseases using established annotation and natural language processing mechanisms. We focus on the track leading up to the development, including strategic motivation and user needs, the development of a proof-of-concept tool, upscaling the idea to a national collaboration project, the development of the final tool and a usability evaluation and subsequent fine-tuning. The tool is a unique visualisation that allows users to benefit with a few clicks from getting the information they require for their needs, and offers novel scientific indicators to characterize (relative) rare disease research activity. We discuss the applications of insights derived from this tool for science policy and to support decision making, and to identify opportunities and potential collaborations and make recommendations for future developments, including a broadening of the scope and discuss potential novel applications.

An Image-Retrieval Method Based on Cross-Hardware Platform Features

Artificial intelligence (AI) models have already achieved great success in fields such as computer vision and natural language processing. However, deploying AI models based on heterogeneous hardware is difficult to ensure accuracy consistency, especially for precision sensitive feature-based image retrieval. In this article, we realize an image-retrieval method based on cross-hardware platform features, aiming to prove that the features of heterogeneous hardware platforms can be mixed, in which the Huawei Atlas 300V and NVIDIA TeslaT4 are used for experiments. First, we compared the decoding differences of heterogeneous hardware, and used CPU software decoding to help hardware decoding improve the decoding success rate. Then, we compared the difference between the Atlas 300V and TeslaT4 chip architectures and tested the differences between the two platform features by calculating feature similarity. In addition, the scaling mode in the pre-processing process was also compared to further analyze the factors affecting feature consistency. Next, the consistency of capture and correlation based on video structure were verified. Finally, the experimental results reveal that the feature results from the TeslaT4 and Atlas 300V can be mixed for image retrieval based on cross-hardware platform features. Consequently, cross-platform image retrieval with low error is realized. Specifically, compared with the Atlas 300V hard and CPU soft decoding, the TeslaT4 hard decoded more than 99% of the image with a decoding pixel maximum difference of +1/−1. From the average of feature similarity, the feature similarity between the Atlas 300V and TeslaT4 exceeds 99%. The difference between the TeslaT4 and Atlas 300V in recall and mAP in feature retrieval is less than 0.1%.

Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach.

Medication safety in residential care facilities is a critical concern, particularly when nonmedical staff provide medication assistance. The complex nature of medication-related incidents in these settings, coupled with the psychological impact on health care providers, underscores the need for effective incident analysis and preventive strategies. A thorough understanding of the root causes, typically through incident-report analysis, is essential for mitigating medication-related incidents. We aimed to develop and evaluate a multilabel classifier using natural language processing to identify factors contributing to medication-related incidents using incident report descriptions from residential care facilities, with a focus on incidents involving nonmedical staff. We analyzed 2143 incident reports, comprising 7121 sentences, from residential care facilities in Japan between April 1, 2015, and March 31, 2016. The incident factors were annotated using sentences based on an established organizational factor model and previous research findings. The following 9 factors were defined: procedure adherence, medicine, resident, resident family, nonmedical staff, medical staff, team, environment, and organizational management. To assess the label criteria, 2 researchers with relevant medical knowledge annotated a subset of 50 reports; the interannotator agreement was measured using Cohen κ. The entire data set was subsequently annotated by 1 researcher. Multiple labels were assigned to each sentence. A multilabel classifier was developed using deep learning models, including 2 Bidirectional Encoder Representations From Transformers (BERT)-type models (Tohoku-BERT and a University of Tokyo Hospital BERT pretrained with Japanese clinical text: UTH-BERT) and an Efficiently Learning Encoder That Classifies Token Replacements Accurately (ELECTRA), pretrained on Japanese text. Both sentence- and report-level training were performed; the performance was evaluated by the F1-score and exact match accuracy through 5-fold cross-validation. Among all 7121 sentences, 1167, 694, 2455, 23, 1905, 46, 195, 1104, and 195 included "procedure adherence," "medicine," "resident," "resident family," "nonmedical staff," "medical staff," "team," "environment," and "organizational management," respectively. Owing to limited labels, "resident family" and "medical staff" were omitted from the model development process. The interannotator agreement values were higher than 0.6 for each label. A total of 10, 278, and 1855 reports contained no, 1, and multiple labels, respectively. The models trained using the report data outperformed those trained using sentences, with macro F1-scores of 0.744, 0.675, and 0.735 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. The report-trained models also demonstrated better exact match accuracy, with 0.411, 0.389, and 0.399 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. Notably, the accuracy was consistent even when the analysis was confined to reports containing multiple labels. The multilabel classifier developed in our study demonstrated potential for identifying various factors associated with medication-related incidents using incident reports from residential care facilities. Thus, this classifier can facilitate prompt analysis of incident factors, thereby contributing to risk management and the development of preventive strategies.

Using structural topic modeling to understand ethnicity-related narratives.

Narrative identity is a promising approach for understanding the content of individuals' ethnic identities but can be limited by the time-intensive nature of human coding and the reliance on preestablished coding systems. The aim of our preregistered study is to elucidate the content of individuals' ethnicity-related experiences using a novel statistical approach. We applied structural topic modeling (STM), a natural language processing tool, to narratives written by an ethnically diverse sample of 1149 young adults about a moment they felt aware of their ethnicity. We identified 14 topics within ethnicity narratives and analyzed how each topic related to both the participant's ethnicity and the human-coded themes of agency and communion. For example, the topic Gained perspective of structural inequality was associated with greater agency, whereas Peer dynamics was associated with greater communion. Ethnic/cultural celebration was associated with both. This study introduces STM as a useful tool for extracting topic content in narrative data and demonstrates how the multi-method assessment of ethnicity narratives provides greater insight into the content of ethnic experiences. These findings contribute to our understanding of contextualized aspects of personality, including the innovative ways we might examine them.

Artificial Intelligence, the Production of Scientific Texts, and the Implications for Sleep Science: Exploring Emerging Paradigms and Perspectives

AbstractThe emergence of artificial intelligence (AI) has revolutionized many fields, including natural language processing, and marks a potential paradigm shift in the way we evaluate knowledge. One significant innovation in this area is ChatGPT, a large language model based on the GPT-3.5 architecture created by OpenAI, with one of its main aims being to aid in general text writing, including scientific texts. Here, we highlight the challenges and opportunities related to using generative AI and discuss both the benefits of its use, such as saving time by streamlining the writing process and reducing the amount of time spent on mundane tasks, and the potential drawbacks, including concerns regarding the accuracy and reliability of the information generated and its ethical use. In respect of both education and the writing of scientific texts, clear rules and objectives and institutional principles must be established for the use of AI. We also consider the positive and negative effects of the use of AI technologies on interpersonal interactions and behavior, and, as sleep scientists, its potential impacts on sleep. Striking a balance between the benefits and potential drawbacks of integrating AI into society demands ongoing research by experts, the wide dissemination of the scientific results, as well as continued public discourse on the subject.

Bridging spherical mixture distributions and word semantic knowledge for Neural Topic Modeling

Neural Topic Modeling has attracted significant attention from the Natural Language Processing community due to its black-box inference property and has made some progress. However, existing approaches have three main drawbacks: (1). hard to integrate external semantic knowledge to improve topic quality; (2). the posterior collapse issue of the Variational Auto-Encoder (VAE) harms the topic diversity; (3). cannot capture the semantic scopes of extracted topics. To address these limitations jointly, we model topics with a mixture of von Mises-Fisher distributions to incorporate word semantic knowledge and propose the Spherical Embedded Topic (SET) model based on the Wasserstein Auto-Encoder. Experiments have been conducted on three widely used text corpora, and the results show that SET obtains more coherent and diverse topics, outperforming the state-of-the-art approaches. Moreover, the ability of SET to capture semantic scopes of topics is also validated by our experiments.