dataesr/scientific-paragraphs-categorization huggingface dataset:
Scores for matches were given on a scale of 100, based on the number of overlapping tokens and the order of tokens. In order to avoid string mismatches, the threshold of score 70 was set. The final dataset contains preprocessed legal text with citations being replaced with IDs. The total number of unique citations were found to be 29010 out of which 7457 were statute citation and 21553 were case citations. The highest statute citation was found to be 11935 and the case citation was 220. We would like to convey that the dataset is part of a proprietary dataset for a commercial application. Hence the dataset cannot be published. For final preprocessing we converted raw text to lowercase, followed by the removal of stopwords, numerals, and punctuations. It is critical to note that no preprocessing is needed to be done on citation IDs.
Data We investigate whether the argumentative structure of a judgment allows us to select the sentences for the guiding principles. Therefore, we inspected existing judgments of the BGH and gathered 100 judgments with extractive guiding principles already formulated by the court. 3 For all of these 100 judgments we determined the exact positions of the respective guiding principles with respect to the argumentative structuring. To avoid overfitting, we used another set of 100 judgments as an unknown test set for the validation of our results. The judgments in this test set did not contain extractive guiding principles but abstractive ones. The reason we chose such a dataset as a test set is that most existing judgments contain abstractive guiding principles. Note that we measure our resulting summaries with the ROUGE metric.
Evaluation For our evaluation, we compared our results to a random selection of sentences, a ranking using only tf*idf values and CasesSummarizer (Table  1 ). We distinguished between the results concerning the judgments containing abstractive and extractive guiding principles and optimized the parameters of our approach separately for each of these datasets. As expected, the ROUGE values on the abstractive judgments were significantly lower than in the extractive judgments and the optimized formula for the extractive version is by no means optimal for the abstractive judgments and vice versa. Compared to a random ranking and a simple tf*df based ranking we could significantly increase the ROUGE score of the results. Especially in comparison to CaseSummarizer we constantly achieved high ROUGE-L scores, which indicates that we are (more) successfully able to identify sentences close to the meaning of the original guiding principles.
Corpus and Method In this section we describe the corpus of Dutch legislation that was our study's object and summarise Katz & Bommarito's method of measuring complexity and our additions to and modifications of their method. According to Katz & Bommarito, the United States Code is only a small portion of existing US law. By contrast, our data set consists of essentially the entire corpus of Dutch legislation limited to acts. To analyse its complexity, structured data is required. Our dataset consisted of a structured XML version of the corpus made available by KOOP, the knowledge and exploitation center for official Dutch government publications The underlying idea of Katz & Bommarito's approach is that complexity can be measured using a knowledge acquisition process where someone wants to decide whether to comply with the law.
Results & Analysis We gauged each measure and calculated the correlation of most in relation to the structural size of legislation. Thereafter, just as  [2] , we used two composites to rank the legislation, with some minor adjustments. These results can be found on Github. 4 In total, 1120 acts were analysed. In this section we analyse our results and compare them to those of Katz & Bommarito. As regards the normalised and unnormalised rankings, it is interesting to observe that as in  [2] , some acts rank similarly in these two rankings while for other acts there are considerable differences in rank (although still within the same region). Apart from this, an absolute comparison between  [2]  and our analysis on the various criteria is not very informative, because of the differences between the Dutch and English languages and the differences in legislation style between the Dutch and US jurisdictions.
Method In this section we describe our method CAJUR, which stands for Concept-based Associative Judgment Recommendation. CAJUR performs judgment tagging for effective recommendation. Figure  1  illustrates CAJUR's design. We start with a judgment database. CAJUR uses a legal taxonomy D as a dictionary of standard tag terms/phrases (A). It employs a tagging module that selects phrases from the taxonomy to create a tag set T J for each judgment J in the database (B&C). Let S () be a seed set of judgments that are relevant to a user's search intent. Each judgment J ′ ∈ S is also given a tag set T J ′ by the tagging module. The user can compose a query () by including or excluding some tags in the tag sets T J ′ 's. The recommender module () will then compare the query against the tag set T J of each judgment J in the database to determine whether J should be recommended to the user ().
Recommender Module Judgment recommendation is made based on a user's search intent. For that, a user maintains a focal tag set F and an exclusion tag set E. Intuitively, we find judgments that mention the concepts expressed by the tags in F but not those in E. As a user explores the judgment database and reads a judgment J, the user can refine the F and E sets by including or excluding the tags given in T J. With the F and E sets, the recommender module retrieves a set of candidate judgments R from the judgment database DB. R is given by: R = {X ∈ DB|F ⊆ T X and E ∩ T X = ∅}. Each judgment X in R is then ranked based on the following scores H 1 to H 3 : (Relevant tag count): H 1 = |F ∩ T r X |. Recall that the relevant tags T r X (generated by the Word2Vec tagger) are those that express concepts directly mentioned in the judgment X.
CAJUR is therefore much more effective in suggesting tags to users in formulating associative queries. This will greatly help users in exploring the judgment database. As an illustrative example, there are a number of PI judgments on HKLII that contain the phrases "upper back" and "neck". For these judgments, CAJUR generates the tag "trapezius muscle", which is a large muscle piece that connects the neck and the upper back. This associative tag helps connect the back-and-neck judgments to those that mention trapezius muscle injuries. The ability of CAJUR in generating associative tags thus help users identify related judgments which would otherwise be missed if only straightforward keyword matching were done. Finally, we evaluate the accuracy of CAJUR's recommendation module. For each of the 25 sampled judgments used in the tagging experiment, we select 1-2 tags into a focal tag set F.
Dataset Our dataset comprises 28,733 legal cases and summaries prepared by attorneys, members of legal societies, or law students and provided by the Canadian Legal Institute (CanLII). 2 As noted our IRC type system for labeling sentences in legal cases and case summaries includes: Issue -Legal question which a court addressed in the case; Conclusion -Court's decision for the corresponding issue; Reason -Sentences that elaborate on why the court reached the Conclusion. All un-annotated sentences are treated as non-IRC sentences. We employed two third-year law school students to annotate sentences from the human-prepared summaries in terms of issues, reasons, and conclusions. They annotated 1049 randomly selected case/summary pairs. Cohen's κ  [18]  metric is used to assess the degree of agreement between two annotators.
Experiment We pre-processed our dataset using the BIO format: we first tokenized all the sentences in summaries and full texts, then assigned the corresponding BIO tags to every token. Those BIO-tagged tokens were then put into the pretrained Longformer  [21]  model for token classification. We chose Longformer over the traditional BERT  [22]  model because of its ability to process longer documents. The maximum input length is 1024 tokens due to the GPU limitation. 3 We decided to segment the full text documents into multiple chunks of length 1024 to avoid information loss. We experimented with two types of Longformer: Lonformer-base-4096 and Longformer-large-4096. 4 We split our datasets of summaries and full texts into 80% training, 10% validation and 10% test sets. I-prefixed token types benefit from more training data, because each annotated sentence has only one beginning token while I-prefixed tokens dominate the rest of the annotated sentence.
Grounding Domain Classes in Norms In order to ground the elements related to the admissibility of an RS that appear in the OntoUML and BPMN diagrams, we align these elements to the norms in force that constitute them. A small fragment of the result of this alignment is shown in Table  1. The table refers to the grounding norms including their LexML norm fragment identifiers, which can be resolved with hyperlinks to https://normas. leg. br/, revealing, when available in that database, the corresponding legal text. For example, we observe the definition of the APPELLATE DECISION event class in article 204 of the Civil Procedure Code, as well as the RS with a definition based on article 14 of federal law n°10. 259/01.
Scenario To test our mechanism, we compiled a scenario involving a hypothetical drone tasked with disabling an enemy's signal intelligence network, which it can achieve by targeting one of the key network points for each district. Complexity is added by introducing variables in the form of the network points' locations, added MA from neutralising enemy personnel around the target, added IH from the amount of civilian persons and objects damaged by an attack, and two types of missiles the drone can select from when engaging. We conceived three different subscenarios with different values for each of the variables, and tested whether our mechanism allows the drone to select the correct (i. e. , IHL-compliant) target and means of engagement. We find that it does. For further details on this scenario and the test results, see the project's github.
Discussion of results In the experiment, the list of available decisions with their evaluations is sent to the intentional agent in a sequence. After the last decision is sent, the system inspects the norms instance embedded in the advisor to see which facts are present. The results of the IHL compliance analysis are presented on the project's github. Although our eFLINTbased normative reasoning mechanism is relatively simple, the results obtained (even for controversial cases) are correct. The problem of balancing was widely discussed in a number of AI and Law papers and legal case-based reasoning in particular. In legal CBR, the objects of comparison are either dimensions (e. g. [17] ) or values (e. g. [18] ). The key difference between our model and the existing ones is in the level of abstraction: both V MA and V CIV have a very abstract character, especially in comparison to dimensions like number of disclosures.
Architecture and Raw Data Selection Figure  1  shows the system architecture for the integrated system. To modularize our presentation, we show the architecture in four blocks, which are indicated on the left margin in Figure  1. The block b3 shows that we need an annotated dataset, m_dataset, to support the machine-learning approaches to train a classifier, MIG. We split the annotated dataset into two parts for training and test to produce the classifier. In order to obtain the annotated dataset, we first need to extract appropriate files from a large collection of judgment documents. In our current work, we use the open data repository of the Judicial Yuan of Taiwan as the main source. We show this repository as TWJY in block b1. There are about 17. 6 million documents in TWJY as of April 2022.
Assuming that our research focuses on a specific category of cases, say burglary. We can use "burglary" as a keyword in criteria_1, and let the retriever extract documents that are related to burglary from TWJY. The resulting raw data set usually includes some documents that do not really fit the research requirements from the legal informatics perspective. Hence, we would consult the expertise of legal experts, and use the filter to remove some files from the raw data set based on criteria_2 to produce the refined data set. After this step, our system offers an interface via which a human expert can select to read a specific document to determine whether to annotate a file in the set. Depending on the research issues of the researchers, the size of refined data set may not be large, even though we have millions of documents in TWJY.
Progressively Powerful Models We gather a sufficient number of m_files to obtain the m_dataset in b3, and run whatever types of machine learning procedures we plan to. In b4, we show that we may use whatever software tools as the algorithm annotator, including JIEBA, spaCy, Stanford NLP tools (STFNLP in Figure  1 ). Whenever possible and desirable, we may use our own model, MIG, as one of the possible tools to annotate the documents. This is not only possible but should be more reasonable when the size of the refined data set is large such that it is unreasonable and inefficient to expect human experts to inspect and annotate all of the data files all at once. Assume that we have ܰ files in the refined data set and that ܰ is large. We can split the refined data set into ‫‬ parts, each having ಿ files.
Introduction These last years, numerous Legaltech projects arose, for instance, to cite a few of them, Lexmachina, Doctrine and Caselaw analytics. The strategy of Legaltechs is clear, based on legal documents and court decisions, they employ machine learning and natural language processing (NLP) techniques to predict the outcome of a dispute: the acceptance or rejection of a claim, the quantum, the legal fees, etc. Accordingly, organizing a massive quantity of unstructured data related to court decisions with the aid of legal experts is crucial. Based on their annotations, aiming at bringing out keywords and sentences (legal norms, names of the parties, jurisdiction, type of claim, facts, outcome, etc. ), the structured data may be stored, before employing NLP models such as models for named entity recognition (NER) in order to automatically annotate all documents.
The creation of a new row in a given task is a dataset query, for instance creating the row "claims of non-recoverable charges, the article 700" in the table claims categories. After that, either developers or annotators can associate a dataset (decisions list) to this row (search by keywords and by standard jurisdiction). Once done, annotators can save annotations of this dataset, and the developer can simultaneously follow the annotation progress and download the dataset. Modules. Scribe is composed of four main components, as shown in Figure  2 : database component, annotation component, download component, and the user authentication component. The database component is responsible for saving data (raw decisions, annotations, tasks classes and users) and for the database exploration with a search engine used when composing datasets.
MongoDB is a Data Base Management System DBMS which is a NoSQL database that allows the structure of the document to be modified  [2] , and to handle big data. The annotation component contains three sub-components, each one dedicated to its specific task (discussed in Section 2) and contains its suitable form. For example, in the NER form, the user selects an entity from the decision text body then all occurrences are automatically selected. In the sectioning form, the user has just to select a decision part that represents the section. As a decision can include multiple claims of the same category, the form of this task allows the annotator to create dynamic similar forms. The three sub-components consume dataset raw decisions (obtained from the search step in the database component) and provide their annotations (also stored in the database).
Django is used to implement the backend logic and Reactjs for the frontend. The download component enables developers to check the available datasets and the annotation progress state (both raw and annotated datasets may be downloaded). The authentication component manages user accounts and login, only authorized people can enter the system and read data. For more explanations, see the demo https: //lawbot. unimes. fr/annotateur. Data. A record in MongoDB is a document, which is a data structure composed of field and value pairs, documents are similar to JSON objects  [2]. MongoDB simplifies annotation saving process and getting datasets in a standard format. It is tolerant about document keys, we can omit some in the documents and place them in others. Figure  3  represents the diagram of the database.
Acknowledgements This work was supported by  JSPS KAKENHI  Grant Numbers,  JP17H06103 ,  JP19H05470  and  JP22H00543  and  JST, AIP Trilateral AI Research , Grant Number  JPMJCR20G4 ,  Japan. Acknowledgements We would like to thank the  Cyberjustice Laboratory  at the  Université de Montréal , the  LexUM Chair on Legal Information  and the Autonomy through  Cyberjustice Technologies  project for supporting this research. Acknowledgements We would like to thank the  Cyberjustice Laboratory  at the  Université de Montréal , the  LexUM Chair on Legal Information  and the Autonomy through  Cyberjustice Technologies  project for supporting this research. Acknowledgements  Hannes Westermann  and  Karim Benyehklef  acknowledge the generous support from the  Cyberjustice Laboratory, LexUM Chair on Legal Information , and Autonomy through  Cyberjustice Technologies  project.
Acknowledgement This work has been supported by grants from the  Autonomy through Cyberjustice Technologies Research Partnership  at the  University of Montreal Cyberjustice Laboratory  and the  National Science Foundation , grant no. 2040490 ,  FAI: Using AI  to  Increase Fairness  by Improving Access to Justice. The  Canadian Legal Information Institute  provided the corpus of paired legal cases and summaries. This work was supported in part by the  University of Pittsburgh Center for Research Computing  through the resources provided. Specifically, this work used the H2P cluster, which is supported by  NSF  award number  OAC-2117681.
Abebi v. Pierre, Michigan (2009), both Lisa and Abebi claimed home state jurisdiction. In Lisa's case, home state was obvious since IMJ lived with Lisa in Virgina for more than six months. Lisa could not exercise the home state jurisdiction because jurisdiction(Cust,Subject,State,exclusivecontinuing), declined(Cust,Subject,State,exclusivecontinuing). jurisdiction(Cust,Subject,State, convenientforum) ← resident(Cust,Subject,State), deferred(Cust,Subject,State,ExState) jurisdiction(Cust,Subject,ExState,exclusivecontinuing). the above rules codifying Home State jurisdiction under UCCJEA will determine that Pierre, as a custodian of Fiia, has home state jurisdiction in Michigan. However, if we add this fact to the database, jurisdiction(pierre,fiia,mississippi,exclusivecontinuing). Pierre will not gain home state jurisdiction in Michigan.
In both studies, the first page of the survey contained a brief introduction to the study, followed by socio-demographic questions. Those questions were then followed by mental health questions that then were followed by the battery of the questionnaires used in this study. No identifying data was requested in both UK and French studies and both online surveys asked for teachers' consent by asking them to click "yes" to continue to the online survey. Moreover, participants in both studies were allowed to drop out at any time by just closing the web browser. They were also fully debriefed at the end and the study took approximately 30 minutes to complete. All data were anonymized by the qualtrics and or Epsylon software that generated a random number for the participants' data. The studies also followed the British Psychological Society's code of ethics for internet research and the code of ethics of the World Medical Association (Declaration of Helsinki) and were approved by the Ethics Research Committees of Nottingham Trent University's Department of Psychology in the UK and the Laboratory of Epsylon in France.
Statistical Analysis Data analyses were performed using SPSS software (version 20). Exploratory data analyses revealed that the data distributions were not normal. We tried to deal with the non-normal distributions by using transformations. The methods employed did not result in normal data distributions. Consequently, we chose to perform non-parametric analyses. Comparisons between the two samples (UK vs. French samples of teachers) were performed using Mann-Whitney analyses. Moreover, correlations between the different variables employed in this study were analysed using Spearman's Rho correlations for each sample. Benjamini and Hochberg's  (34)  false discovery rate (FDR)  (34)  and corrected significance level were applied to the comparisons and correlations to reduce the risk of false positives and of Type I errors.
Indeed, Benjamin and Yekutieli  (35)  argue that the FDR may be the appropriate error rate to control in many applied test problems and was shown to be more poweful than comparable procedures that control the traditional familywise error rate  (35). A power analysis performed with G* power package was conducted to examine the strength of the differences between the uneven groups. It was observed for a sample of 116 individuals a power sensitivity of 99% for the two samples' comparisons for work-related bullying and workplace deviance and its dimensions, which enabled us to reject the null hypothesis and hence to support hypothesis 1. We used the SPSS macro developed by  Hayes (36)  to test our mediation models for the UK and French samples of teachers. In this study, a bootstrapping procedure based on 5,000 resamples was used to calculate a 95% bias-corrected confidence interval (BCCI) around the total indirect effect.
Acknowledgment of Workplace Bullying in the Past 6 Months by UK and French Teachers Concerning the report of workplace bullying in the past 6 months (item 23 of the NAQ) (4) 68% of UK teachers did not acknowledge suffering from workplace bullying in the past 6 months, whereas 17% acknowledged suffering from workplace bullying in the past 6 months but rarely and 11% acknowledged workplace bullying now and then and 5% acknowledged suffering from bullying several times a week. In contrast to this, more French (80%) than UK teachers did not acknowledge suffering from workplace bullying in the past 6 months, whereas 16% acknowledged suffering bullying in the past 6 months but rarely and only 2% acknowledged suffering from workplace bullying several times a week and 2% daily. Also, the majority of the UK teachers perceived their immediate superiors to be the perpetrators of the workplace bullying (N = 19; 73%).
Some items of data were extracted from medical records. All the study participants were invited to fill out a self-questionnaire during their postpartum hospital stay (S1 and S2 Appendices). The primary study outcome was a high level of knowledge about influenza and its vaccine, as assessed using a self-questionnaire based on that described by Yudin et al. [19]. We chose Yudin et al. 's questionnaire because it has been applied in several articles in this field  [14, 15, 18]. To the best of our knowledge, none of the questionnaires used to probe levels of knowledge about vaccination has been psychometrically validated. Our questionnaire was drafted by a multidisciplinary expert group that included obstetricians, infectious disease specialists, general practitioners, and statisticians. We first tested the questionnaire on a randomly selected group of 10 pregnant women receiving antenatal care.