Language theoretic properties of regular DAG languages

Closure properties of linear context-free tree languages with an application to optimality theory

Recently, the question has been raised whether the derivation tree languages of Minimalist grammars (MGs; [14, 16]) are closed under intersection with regular tree languages [4, 5]. Using a variation of a proof technique devised by Thatcher [17], I show that even though closure under intersection does not obtain, it holds for every MG and regular tree language that their intersection is identical to the derivation tree language of some MG modulo category labels. It immediately follows that the same closure property holds with respect to union, relative complement, and certain kinds of linear transductions. Moreover, enriching MGs with the ability to put regular constraints on the shape of their derivation trees does not increase the formalism’s weak generative capacity. This makes it straightforward to implement numerous linguistically motivated constraints on the Move operation.KeywordsMinimalist GrammarsDerivation Tree LanguagesClosure PropertiesRegular Tree LanguagesDerivational Constraints

The development of natural language processing is significant for text emotion analysis because it helps to understand the expression of human emotions in different contexts and provides more accurate semantic understanding and emotion recognition capabilities for intelligent systems. In current natural language processing, sentiment analysis has become a key research field, and it is devoted to developing more accurate and efficient sentiment recognition models to adapt to the growing data scale and semantic complexity. This paper focuses on an overview of contemporary text emotion analysis technology and looks forward to the future development of natural language processing. This paper makes a detailed comparative analysis of the efficiency of different emotion analysis methods from the perspectives of key length, research content, research methods, and results. In the review, the advantages and limitations of various emotion analysis methods will be discussed in detail, including transformer-based and a series of the latest technologies. In addition, the performance differences of different methods of processing large-scale text data will be analyzed in-depth, and their performance in practical applications will be comprehensively evaluated. Finally, the research will discuss the possible future direction of natural language processing in emotion analysis in combination with current research trends and technology development trends to provide helpful enlightenment and guidance for researchers and practitioners in this field.

The generative capacity of combinatory categorial grammars as acceptors of tree languages is investigated. It is demonstrated that the such obtained tree languages can also be generated by simple monadic context-free tree grammars. However, the subclass of pure combinatory categorial grammars cannot even accept all regular tree languages. Additionally, the tree languages accepted by combinatory categorial grammars with limited rule degrees are characterized: If only application rules are allowed, then they can accept only a proper subset of the regular tree languages, whereas they can accept exactly the regular tree languages once first degree composition rules are permitted.

We present effective criteria for first-order definability of regular tree languages. It is known that over words the absence of modulo counting (the “noncounting property”) characterizes the expressive power of first-order logic (McNaughton, Schutzenberger), whereas non-counting regular tree languages exist which are not first-order definable. We present new conditions on regular tree languages (more precisely, on tree automata) which imply nondefinability in first-order logic. One method is based on tree homomorphisms which allow to deduce nondefinability of one tree language from nondefinability of another tree language. Additionly we introduce a structural property of tree automata (the socalled Λ-∨-patterns) which also causes tree languages to be undefinable in first-order logic. Finally, it is shown that this notion does not yet give a complete characterization of first-order logic over trees. The proofs rely on the method of Ehrenfeucht-Fraisse games.

We study the problem of learning regular tree languages from text. We show that the framework of function distinguishability as introduced in our ALT 2000 paper is generalizable from the case of string languages towards tree languages, hence providing a large source of identifiable classes of regular tree languages. Each of these classes can be characterized in various ways. Moreover, we present a generic inference algorithm with polynomial update time and prove its correctness. In this way, we generalize previous works of Angluin, Sakakibara and ourselves. Moreover, we show that this way all regular tree languages can be identified approximately.

Tree-automatic scattered linear orders

Regular tree expressions are a formalism for describing regular tree languages, which can be accepted by a finite tree automaton as a standard model of computation. It was proved that the class of regular tree languages is a proper subclass of tree languages whose linear notations can be accepted by deterministic string pushdown automata. In this paper, we present a new algorithm for transforming regular tree expressions to equivalent real-time height-deterministic pushdown automata that accept the trees in their postfix notation.

A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. The recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. This paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data.

Deep learning prospered as a distinct era of research and fragment of a wider family of machine learning, based on a set of algorithms that strengthen to model high-level abstractions in data. It tries to imitate the human intellect and learns from complicated input data and resolve different types of difficult and complex tasks. Because of Deep Learning, it was successful to deal with different input data types such as text, sound, and images in various fields. Improvement in deep-learning research has already influenced the search for speech recognition, automatic navigation systems, parallel computations, image processing, ImageNet, natural language processing, representation learning, Google translate, etc. Here, we present a review of DL and its applications including the recent development in natural language processing (NLP).

The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate. At most of the times, the intention of fake news is to misinform the people and make manipulated societal insights. The spread of low-quality news in social networking sites has a negative influence upon people as well as the society. In order to overcome the ever-increasing dissemination of fake news, automated detection models are developed using Artificial Intelligence (AI) and Machine Learning (ML) methods. The latest advancements in Deep Learning (DL) models and complex Natural Language Processing (NLP) tasks make the former, a significant solution to achieve Fake News Detection (FND). In this background, the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimization-based Deep Learning Technique for Fake News Detection and Classification (STODL-FNDC) model. The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner. In the proposed STODL-FNDC model, the input data primarily undergoes pre-processing and Glove-based word embedding. Besides, STODL-FNDC model employs Deep Belief Network (DBN) approach for detection as well as classification of fake news. Finally, STO algorithm is utilized after adjusting the hyperparameters involved in DBN model, in an optimal manner. The novelty of the study lies in the design of STO algorithm with DBN model for FND. In order to improve the detection performance of STODL-FNDC technique, a series of simulations was carried out on benchmark datasets. The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.

The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms. To facilitate data mining in this direction, we present a benchmark corpus of 6,086 records manually extracted from 835 electrocatalytic publications, along with an extended corpus with 145,179 records in this article. In this corpus, nine types of knowledge such as material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage are provided by either annotating or extracting. Machine learning algorithms can be applied to the corpus to help scientists find new and effective electrocatalysts. Furthermore, researchers familiar with NLP can use this corpus to design domain-specific named entity recognition (NER) models.

This research explores the nuanced differences in texts produced by AI and those written by humans, aiming to elucidate how language is expressed differently by AI and humans. Through comprehensive statistical data analysis, the study investigates various linguistic traits, patterns of creativity, and potential biases inherent in human-written and AIgenerated texts. The significance of this research lies in its contribution to understanding AI's creative capabilities and its impact on literature, communication, and societal frameworks. By examining a meticulously curated dataset comprising 500K essays spanning diverse topics and genres, generated by LLMs, or written by humans, the study uncovers the deeper layers of linguistic expression and provides insights into the cognitive processes underlying both AI and human-driven textual compositions. The analysis revealed that human-authored essays tend to have a higher total word count on average than AI-generated essays but have a shorter average word length compared to AIgenerated essays, and while both groups exhibit high levels of fluency, the vocabulary diversity of Human authored content is higher than AI generated content. However, AIgenerated essays show a slightly higher level of novelty, suggesting the potential for generating more original content through AI systems. The study also identifies a lower prevalence of gender bias in AI-generated texts but a higher presence of biased topics overall. These findings highlight the strengths and limitations of AI in text generation and the importance of considering multiple approaches for comprehensive analysis. The paper addresses challenges in assessing the language generation capabilities of AI models and emphasizes the importance of datasets that reflect the complexities of human-AI collaborative writing. Through systematic preprocessing and rigorous statistical analysis, this study offers valuable insights into the evolving landscape of AI-generated content and informs future developments in natural language processing (NLP).

We consider the framework of regular tree model checking where sets of configurations of a system are represented by regular tree languages and its dynamics is modeled by a term rewriting system (or a regular tree transducer). We focus on the computation of the reachability set R*(L) where R is a regular tree transducer and L is a regular tree language. The construction of this set is not possible in general. Therefore, we present a general acceleration technique, called regular tree widening which allows to speed up the convergence of iterative fixpoint computations in regular tree model checking. This technique can be applied uniformly to various kinds of transformations. We show the application of our framework to different analysis contexts: verification of parameterized tree networks and data-flow analysis of multithreaded programs. Parametrized networks are modeled by relabeling tree transducers, and multithreaded programs are modeled by term rewriting rules encoding transformations on control structures. We prove that our widening technique can emulate many existing algorithms for special classes of transformations and we show that it can deal with transformations beyond the scope of these algorithms.

The digital revolution has accelerated the development of natural language processing (NLP), yet its benefits remain unevenly distributed across languages. While high-resource languages such as English, Chinese, and Spanish enjoy state-of-the-art NLP applications, the majority of the world’s languages are classified as low-resource, lacking sufficient annotated corpora, computational resources, and linguistic expertise. This imbalance exacerbates digital exclusion and undermines linguistic diversity. Transfer learning has emerged as a powerful paradigm to address these challenges by leveraging pre-trained models on high-resource languages and adapting them to low-resource contexts.