Natural Language Approach Research Articles

Identifying Monetary Policy Shocks: A Natural Language Approach

Identifying Monetary Policy Shocks: A Natural Language Approach

Predicting stock market using natural language processing

PurposePredicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.Design/methodology/approachIn this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.FindingsExperimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.Originality/valueThis study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

IntroductionDemand for emergency ambulances is increasing, therefore it is important that ambulance dispatch is prioritised appropriately. This means accurately identifying which incidents require a lights and sirens (L&S) response and those that do not. For traffic crashes, it can be difficult to identify the needs of patients based on bystander reports during the emergency phone call; as traffic crashes are complex events, often with multiple patients at the same crash with varying medical needs. This study aims to determine how well the text sent to paramedics en-route to the traffic crash scene by the emergency medical dispatcher (EMD), in combination with dispatch codes, can predict the need for a L&S ambulance response to traffic crashes. Methods: A retrospective cohort study was conducted using data from 2014 to 2016 traffic crashes attended by emergency ambulances in Perth, Western Australia. Machine learning algorithms were used to predict the need for a L&S response or not. The features were the Medical Priority Dispatch System (MPDS) determinant codes and EMD text. EMD text was converted for computation using natural language processing (Bag of Words approach). Machine learning algorithms were used to predict the need for a L&S response, defined as where one or more patients (a) died before hospital admission, (b) received L&S transport to hospital, or (c) had one or more high-acuity indicators (based on an a priori list of medications, interventions or observations. Results: There were 11,971 traffic crashes attended by ambulances during the study period, of which 22.3 % were retrospectively determined to have required a L&S response. The model with the highest accuracy was using an Ensemble machine learning algorithm with a score of 0.980 (95 % CI 0.976–0.984). This model predicted the need for an L&S response using both MPDS determinant codes and EMD text. Discussion: We found that a combination of EMD text and MPDS determinate codes can predict which traffic crashes do and do not require a lights and sirens ambulance response to the scene with a high degree of accuracy. Emergency medical services could deploy machine learning algorithms to improve the accuracy of dispatch to traffic crashes, which has the potential to result in improved system efficiency.

Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction.

Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not efficient for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boost empirical performance. Here, we describe a novel Graph2SMILES model that combines the power of Transformer models for text generation with the permutation invariance of molecular graph encoders that mitigates the need for input data augmentation. In our encoder, a directed message passing neural network (D-MPNN) captures local chemical environments, and the global attention encoder allows for long-range and intermolecular interactions, enhanced by graph-aware positional embedding. As an end-to-end architecture, Graph2SMILES can be used as a drop-in replacement for the Transformer in any task involving molecule(s)-to-molecule(s) transformations, which we empirically demonstrate leads to improved performance on existing benchmarks for both retrosynthesis and reaction outcome prediction.

Color in Mandailing Language: A Semantic Study of Connotation

Language is the way of humans perceives their environment. This paper aim described the study of color semantics in Mandailing language. This is because color is one of the most important elements in human life to convey a certain impression and is an interesting topic to analyze because the vocabulary of color in each language is unique. On this occasion, the authors used the Natural Semantic Metal Language approach to describe Colors in Mandailing Language by using semantic studies of connotation. The method used in this research is a qualitative descriptive method that produces word descriptions. The data technique uses the technique of recording, observing and because of the methods used to obtain data so that it is done by recording using language, both spoken language (Mandailing community speech) and writing related to naming color names. Basically, Mandailing culture recognizes 12 colors, namely red (rara), brown (coklat), gray (abu-abu), purple (bunga torung), orange (pinang), pink (merah jambu), green (rata), light green (rata bulung pisang), blue (rata ombun), white (bontar), black (lomlom) and yellow (gorsing). The function of color in the Mandailing language is also used as a means of communication and social adaptation. In terms of meaning, the color in the Mandailing language also has connotative meanings such as dysphemism and eupemism. The research was conducted in Matondang Village - Palas, North Sumatra with 100% ethnic population in Mandailing. The results of this study indicate that the naming of colors in the Mandailing community in Matondang Village is caused by experience in seeing an object so that when seeing the same color as the object, the naming of the color name appears with the object's name.

Twitter Sentiment Analysis of Long COVID Syndrome.

BackgroundLong COVID syndrome originated as a patient phrased terminology which was initially used to describe a group of vague symptoms that persisted after recovering from COVID-19. However, it has moved from a patient lingo to a recognized pathological entity which refers to a group of symptoms that lasts weeks or months after the COVID-19 illness. The novelty of this condition, the inadequacy of research on long COVID syndrome, and its origin as a patient-coined terminology necessitated exploring the disease's sentiments and conversations by analyzing publicly available tweets.MethodTweets were extracted using the Twarc2 tool for tweets in the English language with the keywords (long COVID syndrome, long COVID, post-COVID syndrome, post-acute sequelae of SARS-CoV-2, long-term COVID, long haulers, and chronic COVID syndrome) between March 25, 2022, and April 1, 2022. The analyses included frequency of the keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language approach and the latent Dirichlet allocation algorithm were used to determine the most shared tweet topics, categorize clusters, and identify themes based on keyword analysis.ResultsThe search yielded 62,232 tweets. The tweets were reduced to 10,670 tweets after removing the duplicates. The vast majority of the tweets originated from the United States of America (38%), United Kingdom (30%), and Canada (13%), with the most common hashtags being #longcovid (36%) and #covid (6.36%), and the most frequently used word being people (1.05%). The top three emotions detected by our analysis were trust (11.68%), fear (11.26%), and sadness (9.76%). The sentiment analysis results showed that people have comparable levels of positivity (19.90%) and negativity (18.39%) towards long COVID.ConclusionsOur analysis revealed comparable sentiments about long COVID syndrome, albeit slightly positive. Most tweets connoted trust (positive), fear (negative), and sadness (negative). These emotions were linked with concerns about the infection, pandemic, chronic disability, and governmental policies. We believe this study would be important in guiding information dissemination and governmental policy implementation necessary in tackling long COVID syndrome.

Analysis and classification of privacy-sensitive content in social media posts

User-generated contents often contain private information, even when they are shared publicly on social media and on the web in general. Although many filtering and natural language approaches for automatically detecting obscenities or hate speech have been proposed, determining whether a shared post contains sensitive information is still an open issue. The problem has been addressed by assuming, for instance, that sensitive contents are published anonymously, on anonymous social media platforms or with more restrictive privacy settings, but these assumptions are far from being realistic, since the authors of posts often underestimate or overlook their actual exposure to privacy risks. Hence, in this paper, we address the problem of content sensitivity analysis directly, by presenting and characterizing a new annotated corpus with around ten thousand posts, each one annotated as sensitive or non-sensitive by a pool of experts. We characterize our data with respect to the closely-related problem of self-disclosure, pointing out the main differences between the two tasks. We also present the results of several deep neural network models that outperform previous naive attempts of classifying social media posts according to their sensitivity, and show that state-of-the-art approaches based on anonymity and lexical analysis do not work in realistic application scenarios.

Forecasting Undergraduate Majors: A Natural Language Approach

Committing to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major issuance remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe enrollment histories by using natural-language methods and vector embeddings to forecast terminal major on the basis of course sequences beginning at college entry. We find that (a) a student’s very first enrolled course predicts their major 30 times better than random guessing and more than one-third better than majority-class voting, (b) modeling strategies substantially influence forecasting metrics, and (c) course portfolios vary substantially within majors, such that students with the same major exhibit relatively modest overlap.

Virtual assistant upper respiratory tract infection education based natural language

The high incidence of upper respiratory tract infection (URTI) in Indonesia requires an efficient healthcare solution to maintain human wellbeing. The e-health education model proposed in this paper is a virtual assistant in the form of an interactive question and answer system assistant virtual interactive question answering (AVIQA) with a natural language approach. AVIQA is a form of problem-solving approach to design some aspects of education and consultation in helping parents to recognize symptoms and dealing with several preventive ac tions for toddlers when exposed to Upper Respiratory Tract Infection. The technologies proposed for the development of AVIQA include (i) Representation of sentence meanings to build an URTI knowledge base; (ii) Design of dialogue models for interactive consultation using a combination between information state and frame base model and (iii) development of IQA based on casebase reasoning and semantic role labelling. The purpose of developing this technology is to achieve a system that is capable of assisting the users especially mothers in searching for information, reducing user time compared to reading a document, and providing a good advice for finding the right answers, which then can be constructed from a management model prototype information for the education and independent consultation for users. The final result of this study is e-health education system based Indonesian natural language that has an ability in terms of health consultations especially health of children under five in Acute Respiratory Infection disease. This system is expected to have a significant impact on the ability of a mother to recognize symptoms and deal with children attacked by URTI.

Disabled Children and Communication

The acquisition of the mother tongue is one of the areas that may be problematic for children affected by disability. Problems in both receptive and expressive language development can be seen in children affected by various degrees of disability. It is seen that the natural language approach, which is based on the foundations of social interactionist theory, has been effective in teaching the mother tongue to children who have been affected by inadequacy. This study explains what needs to be done to develop the expressive language of children with mental disabilities who have limited expressive language skills. The study also includes activity suggestions to support children's language development.

Natural language approach for bio-informed architectural education: a biomimetic shell design

Natural language approach for bio-informed architectural education: a biomimetic shell design

On Strengthening the Interest of Architecture Students in Bio-informed Solutions: A Systematic Approach for Learning from Nature

The bio-informed concept, which means “designing by learning from nature’s best ideas” as an approach, method, tool, discipline or strategy, is one of the most important research areas of today. It does not only shape designs, but also is based on collaborative/interactive/creative methods in education and can be integrated with contemporary educational approaches. This paper questions how to translate the bio-knowledge, which can be an effective and useful method for developing designers’ skills such as system-thinking, innovative thinking and problem-based learning, to design education in an easy and understandable way. In this context, the method of determining and applying biological phenomena/systems into architectural design process through the “natural language approach” is investigated. With this research, it is aimed to open the way to reach more innovative and sustainable solutions by establishing a bridge between architectural and biological terminology while creating architectural structures. It has been shown how to increase the biodiversity utilized for bio-informed solutions in the architectural field by proposing a systematic approach to search for biological systems. From this point of view, this study emphasizes the importance of promoting the bio-informed design approach, increasing interdisciplinary relationships and orienting individuals to nature for creativity and sustainability.

Virtual assistant upper respiratory tract infection education based natural language

The high incidence of Upper Respiratory Tract Infection (URTI) in Indonesia requires an efficient healthcare solution to maintain human wellbeing. The e-health education model proposed in this paper is a virtual assistant in the form of an interactive question and answer system (Assistant Virtual Interactive Question Answering = AVIQA) with a natural language approach. AVIQA is a form of problem-solving approach to design some aspects of education and consultation in helping parents to recognize symptoms and dealing with several preventive actions for toddlers when exposed to Upper Respiratory Tract Infection. The technologies proposed for the development of AVIQA include (i) Representation of sentence meanings to build an URTI knowledge base; (ii) Design of dialogue models for interactive consultation using a combination between information state and frame base model and (iii) development of IQA based on casebase reasoning and semantic role labelling. The purpose of developing this technology is to achieve a system that is capable of assisting the users especially mothers in searching for information, reducing user time compared to reading a document, and providing a good advice for finding the right answers, which then can be constructed from a management model prototype information for the education and independent consultation for users. The final result of this study is e-health education system based Indonesian natural language that has an ability in terms of health consultations especially health of children under five in Acute Respiratory Infection disease. This system is expected to have a significant impact on the ability of a mother to recognize symptoms and deal with children attacked by URTI

The Impact of Field of View on Understanding of a Movie Is Reduced by Magnifying Around the Center of Interest.

PurposeMagnification is commonly used to reduce the impact of impaired central vision. However, magnification limits the field of view (FoV) which may make it difficult to follow the story. Most people with normal vision look in about the same place at about the same time, the center of interest (COI), when watching “Hollywood” movies. We hypothesized that if the FoV was centered at the COI, then this view would provide more useful information than either the original image center or an unrelated view location (the COI locations from a different video clip) as the FoV reduced.MethodsThe FoV was varied between 100% (original) and 3%. To measure video comprehension as the FoV reduced, subjects described 30-second video clips in response to two open-ended questions. A computational, natural-language approach was used to provide an information acquisition (IA) score.ResultsThe IA scores reduced as the FoV decreased. When the FoV was around the COI, subjects were better able to understand the content of the video clips (higher IA scores) as the FoV decreased than the other conditions. Thus, magnification around the COI may serve as a better video enhancement approach than simple magnification of the image center.ConclusionsThese results have implications for future image processing and scene viewing, which may help people with central vision loss view directed dynamic visual content (“Hollywood” movies).Translational RelevanceOur results are promising for the use of magnification around the COI as a vision rehabilitation aid for people with central vision loss.

Extracting Structured Genotype Information from Free-Text HLA Reports Using a Rule-Based Approach.

BackgroundHuman leukocyte antigen (HLA) typing is important for transplant patients to prevent a severe mismatch reaction, and the result can also support the diagnosis of various disease or prediction of drug side effects. However, such secondary applications of HLA typing results are limited because they are typically provided in free-text format or PDFs on electronic medical records. We here propose a method to convert HLA genotype information stored in an unstructured format into a reusable structured format by extracting serotype/allele information.MethodsWe queried HLA typing reports from the clinical data warehouse of Seoul National University Hospital (SUPPREME) from 2000 to 2018 as a rule-development data set (64,024 reports) and from the most recent year (6,181 reports) as a test set. We used a rule-based natural language approach using a Python regex function to extract the 1) number of patients in the report, 2) clinical characteristics such as indication of the HLA testing, and 3) precise HLA genotypes. The performance of the rules and codes was evaluated by comparison between the extracted results from the test set and a validation set generated by manual curation.ResultsAmong 11,287 reports for development set and 1,107 for the test set describing HLA typing for a single patient, iterative rule generation developed 124 extracting rules and 8 cleaning rules for HLA genotypes. Application of these rules extracted HLA genotypes with 0.892–0.999 precision and 0.795–0.998 recall for the five HLA genes. The precision and recall of the extracting rules for the number of patients in a report were 0.997 and 0.994 and those for the clinical variable extraction were 0.997 and 0.992, respectively. All extracted HLA alleles and serotypes were transformed according to formal HLA nomenclature by the cleaning rules.ConclusionThe rule-based HLA genotype extraction method shows reliable accuracy. We believe that there are significant number of patients who takes profit when this under-used genetic information will be return to them.

Functional Requirements Specification of E-Tendering Using Natural Language Approach: Towards Innovative Business Transformation

Recently, business transformation towards the used of Information and Communication Technology (ICT) is a necessity toward rapid industries and the paradigm shifted to sustain business competitiveness. The holistic electronic approach is one of business innovations, especially in handling a lot of tender documentations and process in an electronic environment namely as e-Tendering. Unfortunately, the existing tender process transformation in the electronic approach is not properly followed certain standard and guideline, especially in establishing a good e-Tendering functional requirements specification to ensure the organizations would be in the best served. This is important to ensure a good e-Tendering system can be developed by e-Tendering developers based on a good e-Tendering functional requirement specifications. The requirements specification is a process of documenting user and system requirements. Commonly, user and system requirements should be clear, unambiguous, easy to understand, complete, and consistent. In practice, this is difficult to achieve due to interpretation of the requirements in different ways by stakeholders, which are often inherent conflicts and inconsistencies of the requirements. The implementation of the existing e-tendering still remains uncertainties, especially in defining the functional requirements of the e-tendering system. Therefore, this study aims to construct the e-Tendering functional requirement model using requirement template in natural language representation approach. Moreover the development of this system requirement model may provide a consistency to the requirements representation. The study uses UN/CEFACT Business Standard of the e-Tendering Business. The identified functional requirements are designed by using Requirement Template to ensure the reliability and understandability of requirements. Besides, the proposed functional requirements is constructed by adapting the natural language and verified by expert review approaches. As a result, this study proposed a functional requirements specification of the e-Tendering that contains detailed description which can be referred by software practitioners in developing a secure e-tendering system effectively.

Word reordering algorithm for poetry analysis

Automatic poetry analysis is usually examined as subtask of natural language processing but approaches which are good for prose texts are ineffective for poetry due to free semantic characteristics. It allows us to use large corpus for training words order and rather small text corpus amount. In this paper we suggest the way to adapt poetry texts to prose ones by changing word order without loosing of model and also use word2vec features to improve the classifier work.

Approaches to Text Analysis

Text Analysis is a broad term that covers many approaches and technologies. Those initially stemming from the academic sphere have come to enter the commercial, and today there is a wide interplay between the two. A further dichotomy is that between natural language and computational approaches. Over time, approaches have come to draw upon each other, although there are still clear dividing lines and practitioners tend to rely mainly on one approach or the other. This paper seeks to draw out these approaches and give an account of them over time. It also points to future developments where artificial intelligence is increasingly used.

QA 3 : A natural language approach to question answering over RDF data cubes

QA 3 : A natural language approach to question answering over RDF data cubes

NOISE DETECTION IN SOFTWARE REQUIREMENTS SPECIFICATION DOCUMENT USING SPECTRAL CLUSTERING

Requirements engineering phase in software development resulting in a SRS (Software Requirements Specification) document. use of natural language approach in generating such document has some drawbacks that caused 7 common mistakes among the engineer which had been formulated by Meyer as The 7 sins of specifier. One of the 7 common mistakes is noise. This study attempted to detect noise in software requirements with spectral clustering. clustering algorithm working on fewer dimensions compared to others. resulting kappa coefficient is 0.4426 . result showed that the consistency between noise prediction and noise assessment made by three annotators is still low.