Heterogeneous Text Style Control Using Prompts

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

A survey of low-bit large language models: Basics, systems, and algorithms.

Software development relies heavily on manual processes for transforming requirements into software artifacts such as models, source code, or test cases. Requirements are the starting point for these transformations, and they are typically written in natural language. However, hardly any automated tools exist that translate natural language texts into software artifacts. We propose to adapt recent advances in natural language processing and semantic technologies to generate UML models, test cases, and perhaps even source code, from natural language input. Though earlier efforts in automatic programming had limited success, we are now in a situation where extracting meaning from text has made substantial progress. For example, encouraging results for generating UML models from textual requirements have been achieved. It might even be possible to generate executable test cases. An intermediate step would be to generate tests from API documentation (which would also be a useful capability in itself). An even greater advance would be to automate rote coding. In all of these cases, entirely new classes of software tools would be needed to extract and process the semantics inherent in natural language texts, augment them with tacit knowledge from ontologies and domain models, and, where necessary, ask humans to clarify ambiguities. Though speculative, a determined, long-term effort in translating text to software could automate and accelerate software development to an unprecedented degree.

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

Answer set Prolog, or AnsProlog in short, is one of the leading knowledge representation (KR) languages with a large body of theoretical and building block results, several implementations and reasoning and declarative problem solving applications. But it shares the problem associated with knowledge acquisition with all other KR languages; most knowledge is entered manually by people and that is a bottleneck. Recent advances in natural language processing have led to some systems that convert natural language sentences to a logical form. Although these systems are in their infancy, they suggest a direction to overcome the above mentioned knowledge acquisition bottleneck. In this paper we discuss some recent work by us on developing applications that process logical forms of natural language text and use the processed result together with AnsProlog rules to do reasoning and problem solving.

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. while existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.KeywordsTask-oriented conversational botsComplex intent recognitionContext knowledgeSlot value inference

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

Tobler's First Law of Geography (TFL) is one of the key reasons why is special. The law, which states that is related to everything else, but near things are more related than distant things, is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.

Natural Language Processing (NLP) has emerged as a transformative technology within the domain of artificial intelligence, significantly advancing the ability of machines to comprehend and interact with human language. Recent advancements in machine learning algorithms have led to substantial improvements in a variety of NLP applications, including sentiment analysis, machine translation, automated summarization, and question answering. This study presents a novel approach to NLP, which integrates advanced machine learning techniques to enhance performance in these areas. The proposed method achieves a high level of accuracy, with an overall accuracy rate of 97.6%. Additionally, it demonstrates strong performance in error metrics, including a Mean Absolute Error (MAE) of 0.403 and a Root Mean Square Error (RMSE) of 0.203. These results indicate a significant advancement in the precision and effectiveness of language-based tasks. Despite these achievements, challenges such as the management of ambiguous language, the need for extensive annotated datasets, and the demand for real-time processing capabilities remain. This paper aims to explore the current state of NLP driven by machine learning, examining key techniques and methodologies that have propelled its development. The analysis will cover various machine learning approaches applied to NLP, evaluate their impact on different applications, and identify ongoing challenges and future research directions. By providing a comprehensive overview of the interplay between NLP and machine learning, this research seeks to enhance understanding of how these technologies can be leveraged to improve language processing and communication

Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding, and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots, elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry

Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream tasks. Word vector models like Word2Vec, FastText & Glove were widely used over one-hot encoded vectors for years until the advent of deep contextualized embedders. Protein sequences consist of 20 naturally occurring amino acids that can be treated as the language of nature. These amino acids in combinations with each other makeup the biological functions. The choice of vector representation and architecture design for a biological task is highly dependent upon the nature of the task. We utilize unlabelled protein sequences to train a Convolution and Gated Recurrent Network (CGRN) embedder using Masked Language Modeling (MLM) technique that shows significant performance boost under resource constraint setting on two downstream tasks i.e., F1-score(Q8) of 73.1% on Secondary Structure Prediction (SSP) & F1-score of 84% on Intrinsically Disordered Region Prediction (IDRP). We also compare different architectures on downstream tasks to show the impact of the nature of biological task on the performance of the model.

Negation is a universal feature of natural languages. In Brazilian Portuguese, the most commonly used negation particle is não, which can take scope over nouns or verbs. When it takes scope over a verb, “não”can occur in three positions: pre-verbal (NEG1), double negation (NEG2), or post-verbal (NEG3), e.g., “não gosto”, “não gosto não”, “gosto não”(“I do not like it”). From a variationist perspective, these structures are different forms of expressing negation. Pragmatically, they serve distinct communicative functions, such as politeness and modal evaluation. Despite their grammatical acceptability, these forms differ in frequency. NEG1 dominates across Brazilian regions, while NEG2 and NEG3 appear more rarely, suggesting its use is contextually restricted. The low frequency of these structures challenges research, often resulting in subjective, non-generalizable interpretations of verbal negation with não. To address this, we developed negativas, a tool for automatically identifying NEG1, NEG2, and NEG3 in transcribed data. The tool’s development involved four stages: i) analyzing a dataset of 22 interviews from the Falares Sergipanos database, annotated by three linguists, ii) developing the code using the Python language and Natural Language Processing (NLP) techniques, iii) running the tool, iv) evaluating accuracy. Inter-annotator agreement, measured using Fleiss’ Kappa, was moderate (0.57). The tool identified 3,338 instances of não, classifying 2,085 as NEG1, NEG2, or NEG3, achieving a 93% success rate. However, negativas has limitations. NEG1 accounted for 91.5% of identified structures, while NEG2 and NEG3 represented 7.2% and 1.2%, respectively. The tool struggled with NEG2, misclassifying instances as overlapping structures (NEG1/NEG2/NEG3). These challenges stem from the dataset’s lack of punctuation, which in written texts, marks sentence boundaries. In spoken data, prosodic cues serve this purpose, recognized by speakers but not by the tool. This highlights the need for advancements in NLP to better handle the unique features of spoken language data.

We present nlrpBENCH: a new platform and framework to improve soft- ware engineering research as well as teaching with focus on requirements engineering during the software engineering process. It is available on http://nlrp.ipd. kit.edu. Recent advances in natural language processing have made it possible to process textual software requirements automatically, for example checking them for flaws or translating them into software artifacts. This development is particularly fortunate, as the majority of requirements is written in unrestricted natural language. However, many of the tools in in this young area of research have been evaluated only on limited sets of examples, because there is no accepted benchmark that could be used to assess and compare these tools. To improve comparability and thereby accelerate progress, we have begun to assemble nlrpBENCH, a collection of requirements specifications meant both as a challenge for tools and a yardstick for comparison. We have gathered over 50 requirement texts of varying length and difficulty and organized them in benchmark sets. At present, there are two task types: model extrac- tion (e.g., generating UML models) and text correction (e.g., eliminating ambiguities). Each text is accompanied by the expected result and metrics for scoring results. This paper describes the composition of the benchmark and the sources. Due to the brevity of this paper, we omit example tools comparisons which are also available.

We propose integrating conversational user interfaces (CUIs) into Maritime Autonomous Surface Ships (MASS) to facilitate direct communication with other ships without human intervention. The aim is to enable other ships and shore stations to inquire about and discuss the autonomous vessel's status, decisions, and future intentions in natural language, offloading VHF radio conversations for MASS operators. Recent advancements in Natural Language Processing (NLP) technology show promise. This paper presents an experimental study conducted in a ship simulator using a functional prototype CUI system. While the CUI system was capable of providing coherent answers to questions, the results indicate that interacting with the system is less trustworthy than interacting with a human ship officer. Key challenges contributing to this included: (1) a missing connection between the CUI and an autonomy system, leading to inaccurate and incorrect answers, (2) speech that lacked intonation and human-like qualities, and (3) a perceived inability of the system to collaborate. The paper discusses several aspects of utilising CUI systems in a maritime shipping environment and concludes by proposing an approach to develop and implement them safely.