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This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The below-average innovative capacity of the tourism sector raises the question on the potentials of digital business ecosystems (DBEs) to overcome these shortages at a destination level – especially within a smart city environment. Using the example of the German Capital Berlin, this article aims to discuss both the possibilities and inhibitors of innovative knowledge-creation by building scenarios on one specific design option: the integration of digital deep learning (DL) functionalities and traditional organizational learning (OL) processes.

Using the qualitative GABEK-method, major characteristics of a DBE as resource-, platform- and innovation systems are analyzed toward their interactions with the construction of basic action models (as the basic building blocks of knowledge).

Against the background of the research findings, two scenarios are discussed for future evolution of the Berlin DBE, one building on cultural emulation as a trigger for optimized DL functionalities and one following the idea of cultural engineering supported by DL functionalities. Both scenarios focus specifically on the identified systemic inhibitors of innovative capabilities.

While this study highlights the potential of the GABEK method to analyze mental models, separation of explicit and latent models still remains challenging – so does the reconstruction of higher order mental models which require a combined take on interview techniques in the future.

The resulting scenarios innovatively combine concepts from OL theory with the concept of DBE, thus indicating possible pathways into a tourism future where the limitations of human learning capacities could be compensated through the targeted support of general artificial intelligence (AI).

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

Assessing whether a book is relevant or suitable for use in teaching materials is not an easy and haphazard matter, various methods and theories have been offered by researchers in studying this matter. Taking a study of the context of textbooks, researchers found the urgency that textbooks are a foundation for education, socialization and transmission of knowledge and its construction. Researchers offer another approach, namely by using praxeology as a study tool so that the goals of the textbooks previously intended are fulfilled.

The researcher uses a qualitative approach through grounded theory. Grounded theory procedures are designed to develop a well-integrated set of concepts that provide a thorough theoretical explanation of the social phenomena under study. A grounded theory must explain as well as describe. It may also implicitly provide some degree of predictability, but only with respect to certain conditions (Corbin and Strauss, 1990). Document analysis in conducting this research study. Document analysis itself examines systematic procedures for reviewing or evaluating documents, both printed and electronic materials.

Two issues regarding gender acquisition have been investigated in L2 Arabic acquisition studies; the order in which L2 Arabic learners acquire certain grammatical features of the gender system and the effect of L1 on the acquisition of some grammatical features from L2 grammatical gender. Arabic has a two-gender system that classifies all nouns, animate and inanimate, as masculine or feminine. Verbs, nouns, adjectives, personal, demonstrative and relative pronouns related to nouns in the syntactic structure of sentences show gender agreement.

In practice, as a book intended for non-speakers, the book is presented using a general view of linguistic theory. In relation to the gender agreement, the presentation of the book begins and is inserted with the concepts of nouns and verbs. Returning to the praxeology context, First, The Know How (Praxis) explains practice (i.e. the tasks performed and the techniques used). Second, To Know Why or Knowledge (logos) which explains and justifies practice from a technological and theoretical point of view. Answering the first concept, the exercise presented in the book is a concept with three clusters explained at the beginning of the discussion. And the second concept, explained with a task design approach which includes word categorization by separating masculine and feminine word forms.

Practically, this research obtains perspectives studied from a textbook, namely the Arabic gender agreement is presented with various examples of noun contexts; textbook authors present book concepts in a particular way with regard to curriculum features and this task design affects student performance, and which approach is more effective for developing student understanding. Empirically, the material is in line with the formulation of competency standards for non-Arabic speakers in Indonesia.

With this computational search, the researcher found a novelty that was considered accurate by taking the praxeology context as a review in the analysis of non-speaking Arabic textbooks, especially in the year 2022 (last data collection in September) there has been no study on this context. So then, the researcher finds other interests in that praxeology can examine more broadly parts of the task of the contents of the book with the approach of relevant linguistic theories.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

The current paper aims at exploring negative aspects in reviews about Airbnb listings in Athens, Greece.

The aspect-based sentiment approach (ABSA), a subset of sentiment analysis, is used. The study analyzed 8,200 reviews, which had at least one negative aspect. Based on dependency parsing, noun phrases were extracted, and the underlying grammar relationships were used to identify aspect and sentiment terms.

The extracted aspect terms were classified into three broad categories, i.e. the location, the amenities and the host. To each of them the associated sentiment was assigned. Based on the results, Airbnb properties could focus on certain aspects related to negative sentiments in order to minimize negative reviews and increase customer satisfaction.

The study employs the ABSA, which offers more advantages in order to identify multiple conflicting sentiments in Airbnb comments, which is the limitation of the traditional sentiment analysis method.