Abstract
The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. ...The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments.
To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large ...amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area.
Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape.
To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers.
This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details).
This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners.
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct ...definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a taxonomy of reasoning. Practically, we conduct a comprehensive literature review on natural language reasoning in NLP, mainly covering classical logical reasoning, natural language inference, multi-hop question answering, and commonsense reasoning. The paper also identifies and views backward reasoning, a powerful paradigm for multi-step reasoning, and introduces defeasible reasoning as one of the most important future directions in natural language reasoning research. We focus on single-modality unstructured natural language text, excluding neuro-symbolic research and mathematical reasoning.
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing ...models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.
•NLP algorithm can detect features of formal thought disorder (FTD).•Deep contextual word representations may be used to improve detection of the FTD.•NLP accuracy is comparable to observer’s ...ratings.
Computational linguistics has enabled the introduction of objective tools that measure some of the symptoms of schizophrenia, including the coherence of speech associated with formal thought disorder (FTD). Our goal was to investigate whether neural network based utterance embeddings are more accurate in detecting FTD than models based on individual indicators. The present research used a comprehensive Embeddings from Language Models (ELMo) approach to represent interviews with patients suffering from schizophrenia (N=35) and with healthy people (N=35). We compared its results to the approach described by Bedi et al. (2015), referred to here as the coherence model. Evaluations were also performed by a clinician using the Scale for the Assessment of Thought, Language and Communication (TLC). Using all six TLC questions the ELMo obtained an accuracy of 80% in distinguishing patients from healthy people. Previously used coherence models were less accurate at 70%. The classifying clinician was accurate 74% of the time. Our analysis shows that both ELMo and TLC are sensitive to the symptoms of disorganization in patients. In this study methods using text representations from language models were more accurate than those based solely on the assessment of FTD, and can be used as measures of disordered language that complement human clinical ratings.
Hate Speech in social media is a complex phenomenon, whose detection has recently gained significant traction in the Natural Language Processing community, as attested by several recent review works. ...Annotated corpora and benchmarks are key resources, considering the vast number of supervised approaches that have been proposed. Lexica play an important role as well for the development of hate speech detection systems. In this review, we systematically analyze the resources made available by the community at large, including their development methodology, topical focus, language coverage, and other factors. The results of our analysis highlight a heterogeneous, growing landscape, marked by several issues and venues for improvement.
Natural language processing (NLP) has experienced significant growth in recent years and shows potential for broad impacts in scientific research and clinical practice.
This study comprises an ...exploration of the role of NLP in scientific research and its subsequent effects on traditional publication practices, as well as an evaluation of the opportunities and challenges offered by large language models (LLM) and a reflection on necessary paradigm shifts in research culture.
Current LLMs, such as ChatGPT, and their potential applications were compared and assessed. An analysis of the literature and case studies on the integration of LLMs into scientific and clinical practice was conducted.
LLMs provide enhanced access to and processing capabilities of text-based information and represent a vast potential for (medical) research as well as daily clinical practice. Chat-based LLMs enable efficient completion of often time-consuming tasks, but due to their tendency for hallucinations, have a significant limitation. Current developments require critical examination and a paradigm shift to fully exploit the benefits of LLMs and minimize potential risks.
Natural Language Processing (NLP) is a branch of artificial intelligence that involves the design and implementation of systems and algorithms able to interact through human language. Thanks to the ...recent advances of deep learning, NLP applications have received an unprecedented boost in performance. In this paper, we present a survey of the application of deep learning techniques in NLP, with a focus on the various tasks where deep learning is demonstrating stronger impact. Additionally, we explore, describe, and revise the main resources in NLP research, including software, hardware, and popular corpora. Finally, we emphasize the main limits of deep learning in NLP and current research directions.
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