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22 December 2019

Application of an Ecosystem Methodology Based on Legal Language Processing for the Transformation of Court Decisions and Legal Opinions into Open Data

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,
and
1
Department of Computer Engineering and Informatics, University of Patras, 265 04 Patras, Greece
2
School of Law, National and Kapodistrian University of Athens, 106 53 Athens, Greece
*
Author to whom correspondence should be addressed.
Information2020, 11(1), 10;https://doi.org/10.3390/info11010010
This article belongs to the Section Information Systems
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Abstract

Regulation of modern societies requires the generation of large sets of heterogeneous legal documents: bills, acts, decrees, administrative decisions, court decisions, legal opinions, circulars, etc. More and more legal publishing bodies publish these documents online, although usually in formats that are not machine-readable and without following Open Data principles. Until an open by default generation and publication process is employed, ex-post transformation of legal documents into Legal Open Data is required. Since manual transformation is a time-consuming and costly process, automated methods need to be applied. While some research efforts toward the automation of the transformation process exist, the alignment of such approaches with proposed Open Data methodologies in order to promote data exploitation is still an open issue. In this paper, we present a methodology aligned to the Open Data ecosystem approach for the automated transformation of Greek court decisions and legal opinions into Legal Open Data that builds on legal language processing methods and tools. We show that this approach produces Legal Open Data of satisfying quality while highly reducing the need for manual intervention.

1. Introduction

Due to increased complexity of the political, economic, and social environment in which people live and act, modern societies have set up institutions that produce constantly growing sets of legal documents for their regulation. These sets include documents of primary legislation (e.g., the constitution and the laws), documents of secondary legislation (e.g., ministerial decisions), court decisions, legal opinions, and even documents released from public administration bodies regarding the application of the law (e.g., administrative circulars). As technology advances and the web expands, these documents, originally printed on paper, are now available online in most countries. Open access to law is a basis for democratic society and computer technology facilitates public access to legal resources at a low cost [1]. Peruginelli describes law as “the operating system of our society”, stating that “the nature of law is so pervasive, it becomes essential for everybody to know about it” [2]. Initiatives such as the “free access to law movement” [3] and public accessibility projects will probably result in governments providing online access to even more legal information resources in the future [4]. The volume of these heterogeneous documents is expected to constantly rise, forming datasets that show many of the characteristics that define Big Data. Even if their volume cannot be compared to the volume of data collected from other sources such as social networks, their manual analysis and processing is prohibitive and automated approaches are needed to undertake such tasks [5]. It is noteworthy that some researchers explicitly classify legal documents as Big Data since they meet at least two of the major aspects of Big Data: volume and variety [6]. While several governments are taking steps to apply semantic web technologies in the legal publishing domain (e.g., the legislation.gov.uk platform where UK legislation is published in XML and RDF) [7], in most countries these documents are usually made available in unstructured or poorly structured formats; for example, as PDF files, HTML documents, or plain text.
On the other hand, the current trend of Open Data requires that government data (legal documents being a special category of them) are published without technical and legal impediments, in structured and machine-processable formats, and under an open license. Janssen et al. [8] have discussed in detail the expected benefits from the adoption of the Open Data model: transparency, democratic accountability, economic growth, stimulation of innovation, creation of new services, improved decision and policy making, equal access to data, etc. To ensure these benefits of Open Data, researchers propose the “transparency-by-design” model, an organization model for data publication systems, which is expected to contribute to the automatic opening of data [9]. However, this is not yet the case in the legal domain, and the ideal scenario, where legal publishing systems are organized in such a way that legal information is generated in the form of Open Data by default, still seems distant. The availability of data in non-machine-processable formats, and the need for manual processing and conversion are identified as significant barriers for the use of Open Data, while other impediments include data heterogeneity and fragmentation and the lack of metadata for their description [10].
As Agnoloni et al. note in [4], availability of data in structured formats such as XML is a fundamental prerequisite for the interoperability of data originating from heterogeneous sources and the interoperability of applications that exploit them. Moreover, XML markup and semantic annotation facilitates the transition from documents to data [4]. The need for structured and formally described legal documents has led to the development of legal editors, which allow their users to draft new legal documents or to markup existing ones [11]. As a result, legislative bodies may adopt these tools in order to publish structured versions of their newly generated documents. However, the conversion of existing documents to a structured format is a problem that cannot be tackled manually, as the manual markup of such large sets of texts is a laborious and extremely time-consuming process [12]. Since legal language is natural language, and legal documents often have a standard structure and follow predefined common patterns, Natural Language Processing (NLP) tools can be used to automate the conversion process. However, automating tasks related to the processing of legal texts is not as easy as one could expect and sometimes even humans face difficulties in addressing these tasks (for example Dragoni et al. mention the identification of legal rules and conditions as such a task [13]), since legal language combines complex phrases with complex sentence syntax [14]. While NLP is a mature subfield of Artificial Intelligence and several techniques with satisfying performance are available for accomplishing linguistic tasks (e.g., stemming, tokenizing, part-of-speech tagging, sentence splitting, etc.) for some types of text (e.g., narrative or newspaper text), their application to legal text is challenging due to the complexity of legal language [15].
As we show in the Related Work section, some research efforts that focus on automated transformation of legal documents into open formats have already taken place. However, these efforts are not based on the established theoretical frameworks for Open Data exploitation, and in order to fill this gap, we present a methodology aligned with the so-called Open Data ecosystem approach. Our work is based on legal language parsing and processing for the transformation of legal documents available on the web, but residing in heterogeneous sources and in non-machine-readable formats, into Legal Open Data. While our approach can be adapted and applied for any type of legal documents, we focus on Greek court decisions and legal opinions and we show that treating legal language as a Domain Specific Language (DSL) and defining grammar rules for parsing legal texts provides satisfying results, significantly reducing the need for manual intervention, and thus paving the way for fully automating the transformation process in the future.

3. Open Data Methodology

Several models have been proposed to describe the Open Data lifecycle (i.e., the processes and practices related to data handling from creation to exploitation), each one having its own strengths and weaknesses [21]. Currently, more researchers have been highlighting the need to replace traditional Open Data practices with approaches that focus on the larger Open Data environment, known as the Open Data ecosystem, in order to create value from Open Data. For an extensive literature review on this subject, one may refer to [22]. Following this “ecosystem” approach, the researchers in [23] combined steps from existing models in an extended Open Data lifecycle, which consists of the following stages: create/gather, pre-process, curate, store/obtain, publish, retrieve/acquire, process, use, collaborate with users, and provide feedback. As Open Data ecosystems involve a data provider and a data user level [22], the researchers imagined that the above stages form two interdependent cycles [21]: the inner one referring to the data provider level (create, preprocess, curate, store/obtain, publish) and the outer one to the data user level (retrieve/acquire, process, use, collaborate with users, and provide feedback).
Similarly, Lnenicka and Komarkova [24] focusing on Big and Open Linked Data (BOLD) ecosystems, identified several types of involved stakeholders (ecosystem orchestrator, service provider, application provider, data producer, data publisher, data user, and data prosumer) and proposed a BOLD analytics lifecycle consisting of six phases: acquisition and extraction, management and preparation, storage and archiving, processing and analysis, visualization and use, publication, sharing, and reuse. Each stakeholder participates in different phases of the model with a different role in each phase.
The work presented in this paper is part of a wider project [25] which aims to collect a variety of Greek legal documents from available heterogeneous sources and transform them into Legal Open Data. In order to fully exploit the benefits of Open Data, our methodological approach was designed having the ecosystem approach in mind and is aligned with the models presented above. Previous research efforts do not take into account this proposed methodology and we believe that our work fills in this gap. Table 1 shows the steps of our approach and how they match the stages of the extended Open Data lifecycle (inner cycle for data publishers) and the BOLD analytics lifecycle. As we are not actually the creators of the legal resources, but we are generating structured legal data from the large datasets of unstructured legal documents that we collect, our role is more that of a “transforming” publisher. Since the two models describe different roles for the involved stakeholders of the ecosystem, we set our role to more accurately represent the data provider role from the extended Open Data lifecycle and the data publisher role from the BOLD analytics lifecycle. As a result, as part of our methodology we define steps and actions related to the phases that correspond to these roles.
Table 1. Steps of our methodology and mapping to the stages of the Extended Open Data lifecycle and the BOLD (Big and Open Linked Data) analytics lifecycle.
The steps of our methodology for this Legal Open Data project are described below:
  • Identify online sources of legal documents: While publishing of data in fragmented sources is considered to pose severe impediments to their reuse [10], legal documents are usually published through a variety of web platforms and there is not a single access point. Moreover, sometimes the same documents reside in more than one web locations. Consequently, a first step in the process is the identification and evaluation of available online sources of legal documents.
  • Collect documents: During this step, legal documents are gathered from the selected sources of the previous step. This task requires the development of software that takes advantage of available APIs and web services or, in the frequent case that such services are not provided, the implementation of web scrapers. Unfortunately, in the latter case, apart from the required effort for the development of a different script for each source, there is also the disadvantage that even a small change in the source’s structure may turn the scraper not functional.
  • Transform to plain text: Legal documents are often published in closed, not machine-processable formats, such as PDF or Microsoft Word. In order to extract the legal content from these files, it is required to convert them in plain text. This step includes also pre-processing tasks for the removal of erroneous or irrelevant elements (e.g., page numbers, footers, headers, etc.) injected from the conversion process.
  • Modelling: Several standards for the modeling of legal resources have been developed as part either of national or international initiatives [26]. Each standard defines a set of metadata and structural and semantic elements. In this step, the appropriate model must be adopted according to the project requirements. In our case, the Akoma Ntoso document model is used and the reasons for this choice are explained in Section 5.
  • Transform to structured format: During this step, NLP techniques are applied in order to identify the metadata of the legal schema (in case they are not available from the original source) and the structural parts of the documents. In addition, semantic information about elements of the documents (e.g., legal references, persons, locations, dates, etc.) is extracted. The legal language processing approach we followed to accomplish the transformation is presented in Section 6.
  • Store: This step involves decisions related to the storage of the generated open datasets. Data can be uploaded to existing repositories (e.g., national data portals) or to newly deployed data managements systems. Moreover, in this step the datasets can get linked to other available open datasets and APIs or web services providing access to them can be developed.
  • Publish: In this final step, legal issues related to the license under which data are published and to intellectual properties rights are covered.
The above methodology was implemented in the framework of our research and the architecture of the system that we designed is shown in Figure 1. In the rest of the paper, we mainly focus on the components that are related to steps 1 and 2 (Section 4—Data collectors), 4 (Section 5—Modelling) and 5 (Section 6—Legal Text Processor) of the methodology. Some information is also provided for the implementation of the other steps, however not in full detail, since they are related more to technical decisions and do not present significant research interest.
Figure 1. A proposed implementation of our methodology.

7. Components’ Integration and Documents Transformation

Following the architecture shown in Figure 1, all distinct components were integrated into a functional system developed using the python programming language. The system was setup in a server equipped with a 4-core Intel Xeon E3-1220v6 CPU @3,00GHz and 8GB RAM, which can be considered a low-end solution in terms of computational power. After collecting the available documents from the selected legal sources, transforming them into plain text and applying necessary pre-processing, the resulting set of 142398 text files was used to feed the legal language processor pipeline.
Using parallel processing (with the GNU Parallel tool [46]) we were able to take advantage of all CPU cores at the same time for the processing of the legal texts. The process is as follows: each file initially passes through the Lexer and the resulting tokens are given as input to the Parser. The resulting parse tree is enriched with NER tags for the extracted entities, and the output is provided as input to the XML encoder. This module traverses the parse tree and takes advantage of the nodes’ labels in order to identify the parts that form the elements of the XML file. Especially for metadata elements, metadata collected from the legal sources are combined with the necessary nodes of the parse tree in order to create the content of the <meta> element. Finally, the XML file is validated against the Akoma Ntoso Schema.
The transformation process lasted for almost 15 d. The average time for the transformation of each document was 29.91 s. However, there is room for even better performance by using Java instead of Python, since the Java target of ANTLR can be up to 20 times faster than the Python target and parsing occupies almost 95% of each document processing time. Finally, 127,061 XML files were found to be valid Akoma Ntoso documents, a number that accounts for a percentage of 89.23% of the total number of documents (91.47% of judgments of the Council of State, 83.85% of judgments of the Supreme Civil and Criminal Court and 77.30% of legal opinions). Most validation failures are found for documents published in specific years (e.g., 1995–1998 and 2009 for the Council of State and 2014–2017 for the Supreme Civil and Criminal Court), for which there are no documents in our training set or in the opposite case, the selected documents were not representative of the drafting style followed during these periods. Consequently, a more careful selection of the training set, instead of the random selection that we adopted, could lead to even better results.
The source code of our system along with a sample collection of automatically generated XML files are available in a Github repository: https://github.com/OpenLawsGR/judgments2AKN. Moreover, in Table 11 we provide a short representative example of a court decision (part of the text was omitted due to space limitations) marked up in the Akoma Ntoso standard.
Table 11. Example of a court decision (some parts are omitted due to space limitations) automatically marked up in Akoma Ntoso. Different colors are used to denote the distinct structural and semantic elements.

8. Evaluation Results

In order to evaluate the performance of our approach, we created a second set of 75 randomly selected documents, 25 from each legal body, as shown in Table 12. These documents were automatically transformed into Akoma Ntoso XML files following our approach, and a legal expert was asked to manually assess the performance of our method for legal structure identification and legal citations extraction and resolution.
Table 12. Word count analysis of the set of documents used for evaluation purposes.
To evaluate the quality of the structural markup, we followed the same approach as Sannier et al. in [12]: we classified the main structural elements (header, introduction, background, motivation, decision, conclusions) as fully correct (FC) if no manual corrections were needed, partially correct (PC) if the elements were present but corrections were needed and missed (M) if they were absent. The results of the evaluation of the basic structural elements’ annotation are shown in Table 13. In our case, where the legal texts follow the Judgment type structure, there is a relatively small number of main structural elements in each document, in contrast to legal acts, where there exist usually dozens of chapters, articles, paragraphs, etc. The value of the metric Q that Sannier, et al. proposed (Q = FC/(FC+PC+M)), which denotes the proportion of fully correct elements over the total number of elements is 94.24%, meaning that the additional manual effort required to fix problems of the automated process related to the structure of the generated files is considerably low, taking also into account that partially correct elements (4.15%) require less effort to fix than missed elements (1,61%). Consequently, our approach performs extremely well regarding legal structure identification, even for documents like legal opinions, where drafting rules are much more loose and each author follows their own style.
Table 13. Evaluation results regarding structural elements identification.
In order to evaluate the quality of our legal citations’ extraction and resolution method, we considered as correct only the detected legal references that were resolved correctly. Consequently, partially detected references or references with a wrong or partially correct value of the href attribute were classified as erroneous. We should note again that in this research effort we did not work with incomplete references and as a result such references were not taken into account in the evaluation process. The results of the assessment are shown in Table 14, while Table 15 contains the respective values of Precision, Recall, and F1 Score.
Table 14. Evaluation results regarding legal references identification and resolution.
Table 15. Precision, recall, and F1 Score for legal references identification and resolution.
Several interesting remarks emerge from the inspection of these tables. First of all, the mean number of complete legal references is 22.24 references per document and considering that there are also many incomplete references, we confirm that court decisions and legal opinions are highly interconnected with each other and with other legal documents. It is obvious that in case of manual processing, a considerable workload is required for legal references’ detection and markup. Legal opinions contain almost 2.5 times more references than court decisions, which is expected when considering the length of each document’s type. Our grammar seems to perform very well regarding simple references, since almost 90% of them were successfully detected and resolved. The performance drops significantly for complex references to almost 78%, since there are much more citation styles for this category, while in total 81.29% (a percentage corresponding to the Recall metric) of all complete references were correctly extracted and resolved. Performance varies slightly between different legal bodies and documents from the Council of State show less variation in citing patterns than documents from the other two bodies. We should note that in contrast to legislative drafting, there are usually no official instructions for legal referencing when drafting court decisions and legal opinions, and as a result several different referencing styles can be found in these texts. Moreover, we noticed that the identification and resolution process was often failing due to typos or misspelling (e.g., mix of look-alike characters of the Latin and Greek alphabet or injection of unnecessary punctuation marks). Other reasons for failures in the evaluation process include the existence of extremely complex or ambiguous references (sometimes even difficult for a non-legal expert to resolve) and the use of rare citation patterns. The performance would be much higher if we had taken into account partially detected and resolved references. Considering that our base set that was used to identify the referencing patterns and create the grammar rules consisted of only 75 documents, we believe that the performance of the approach is satisfying and reduces significantly human effort to markup the rest of the legal references. A larger set of documents would have revealed more citation patterns, allowing for a more extended grammar and probably better evaluation results, since another frequent reason for failures was that some referencing patterns in the evaluation set were not present in the base set. At the same time, a larger base set would require higher workload and more time to inspect the documents, detect the patterns and create the set of grammar rules. Finally, we should highlight that the approach produced an extremely small number of false positive results (only six), which is the reason for the high value of the Precision metric.

9. Discussion

As discussed in the Introduction section, it is a common belief among researchers that releasing available government data as Open Data is a prerequisite for gaining the expected benefits. However, a long way is still lying ahead regarding data liberation, since in 2016 only 10% of data published at a global level were estimated to get released in an open format, a fact that significantly limits the potential for reuse and exploitation [47]. The situation seems even more disappointing when inspecting the application of the Open Data principles in the legal domain. According to the 4th edition of the Open Data Barometer [48], a global report of the World Wide Web Foundation on Open Government Data, while legislative datasets are considered important for government accountability, datasets related to legislation consist only 3% of open datasets published by all governments. Six years ago, Marsden was stressing the need for Big Open Legal Data that would contribute to better access to legislation and better governance [49], however Open Data surveys like the Open Data Barometer show that we have not yet achieved this goal.
On the other hand, simple opening of data is not enough for creating value from their exploitation. As Peristeras notes at the foreword of the book “Open Data Exposed” [50], there is additionally the need for policies that guarantee data quality, promote interoperability, and ensure compliance to established standards.
Working towards surpassing those problems that hinder the exploitation of available legal documents in digital format, we have so far focused our research on developing a methodology for automating the transformation of the existing corpus of legal documents into Open Data, taking advantage of the structural and semantic features of legal texts and the available natural language processing tools. Such an automation is necessary in order to reduce the need for human involvement in the process, saving valuable resources. As van Opijnen has noted in [51], before solving more challenging problems of the legal informatics domain, such as legal reasoning, we should fix the architectural flaws related to legal information publishing (e.g., formats, standardization of resources identification, interconnection, etc.).
Answering the dilemma between rule-based and machine learning approaches, we decided to proceed with a rule-based implementation. While machine learning is much more attractive to the academic community for information extraction related tasks, rule-based systems are easier to comprehend, maintain, incorporate domain knowledge and debug and are more popular between industrial vendors, even if recall and precision are often lower and rules’ development is a tedious manual task [52].
In this context, we believe that our work makes several contributions:
Unlike other similar efforts found in the relevant literature, we wanted to incorporate our approach into well-established theoretical Open Data frameworks that are designed to promote data exploitation. The proposed methodology of our project is aligned with the ecosystem approach from the publisher perspective, an approach which, as already discussed, is considered appropriate to create value from Open Data, while at the same time contributes to higher Open Data maturity in practices of both data providers and consumers [23]. More specifically, in Section 2 we showed how the steps of our methodology are aligned with two ecosystem models: the extended Open Data lifecycle and the BOLD analytics lifecycle. Our approach is designed to cover the specific characteristics of documents produced within the Greek legal system; however, it can be easily adapted to cover the features and peculiarities of other legal systems. Even if one may argue that the Greek legal system is of little interest to the global legal research community due to the small size of the population that is able to read legal documents written in the Greek language, such projects are of high importance and provide useful insights, since according to Peristeras the application of the Open Data movement outside the Anglo-Saxon context “revealed specificities and special characteristics based on cultural, institutional and organisational factors that need to be carefully considered” [50].
Standardization and interoperability are important requirements for promoting innovation in the big data ecosystem that derives from public sector generated information [53]. Despite the existing efforts for legal documents standardization, the level of interoperability in this field is still low, while the poor level of semantic information attached to documents prevents conceptual interconnection and sharing of information [54]. Our work, contrary to a number of other research efforts that use custom solutions and schemas for legal data representation, builds on well-established international standards of the legal semantic web, such as the Akoma Ntoso document model, the Akoma Ntoso naming convention and ECLI. Akoma Ntoso provides flexibility and allows for local customization, while at the same time ensuring interoperability by defining common building blocks that can be applied to different judiciary systems.
Sannier et al. [12] have underlined the lack of research focusing on automatic structural markup of legal documents at large scale and stressed the need for more efforts in this field. Our work comes to fill this gap, resulting at a large volume of court decisions and legal opinions being automatically marked up in the Akoma Ntoso format using legal language processing techniques. In total, more than 140,000 documents were automatically processed and marked up in XML in approximately 15 d (as already discussed, this performance could be much better if the Java instance of ANTLR was used) with a percentage of valid XML files around 90%, while manual markup would require several man-months or even man-years and would result at a much higher cost (we should note that the server used for the processing of the legal texts costs less than 1800 Euros). Moreover, we conducted an evaluation of the approach that treats legal language as a Domain Specific Language, since Koniaris et al. [20], who also employed the DSL approach in a similar research, do not provide evaluation results on the quality of the transformation. The quality evaluation of the markup that we performed confirms the conclusion of Sannier et al. [12], according to which automatic approaches for the markup are effective and the amount of required manual effort is relatively low. The evaluation results that we presented showed that around 95% of the effort needed to markup the structural parts of the legal documents can be fully undertaken by the NLP processing software, while only 1% of the effort is exclusively manual and around 4% is a combination of manual and machine work. Similarly, more than 80% of the effort needed to successfully markup complete legal references can be accomplished automatically, while a considerable portion of citations are detected but not resolved. As a result, the automated completion of the markup tasks takes on average around half a minute and only minimal manual intervention is needed to correct software failures, while our experience showed that marking up legal documents manually with an Akoma Ntoso editor requires up to 30 min depending on the length of the document. We should not forget that the designed pipeline includes also other time-consuming steps, such as preprocessing and validation, which when performed manually increase even more the required processing time. However, more research is needed to fully automate the process and a semi-automated approach with low manual intervention seems currently more realistic. For the time being, our work confirmed that automation cannot be fully accurate mostly due to the complexity and variation of legal language used and manual work remains inevitable, a conclusion also highlighted in [12].
Our work confirmed most of the causes identified in [12] as barriers for the automation of the transformation process: heterogeneity of drafting styles, rare patterns for cross-references, incorrect use of characters that look identical, typographical errors, line breaks used for layout purposes etc. Since most of the above problems are not trivial to solve and require time investment in the pre-processing stage, confirming their impact to the quality of transformation is beneficial, as it highlights to candidate Open Data producers the need for setting up a strategy to tackle with them.
Transformation of data originally available in not machine-processable formats into open format and creation of descriptive metadata is a process that requires considerable costs that are part of the so-called “adaptation costs” [50]. According to Zuiderwijk et al. [33] the creation and maintenance of metadata is time-consuming and requires high investments and costs. Designing methodologies and tools for the automation of the process highly contributes to the removal of these Open Data barriers.
Court decisions are important base materials for legal professionals and other involved stakeholders in the legal domain, however their re-use as open data is still limited mainly due to technical barriers, such as documents’ format [55]. The EU Council, in its Conclusions on the Online Publication of Court Decisions, urge for the publication of court decisions in machine readable format, along with their metadata, a practice that could facilitate data re-use [27]. Our work is one of the few in the research literature focusing on the publication of court decisions as Open Data. Additionally, as far as we know, it is the only research effort considering transformation of legal opinions (a legal document type with its own distinct characteristics), which are important documents for the application of the law, into an open format.
It is worth mentioning that one limitation of our work is that the evaluation results are not validated against other automated transformation approaches. Such a task would require the application of other proposed parsing and processing methodologies on the same set of legal documents; unfortunately, this is not currently feasible due to the following reasons:
Approaches adapted to other legal systems and languages cannot be directly applied to our dataset, due to the peculiarities and the specific features of the Greek legal domain (including the unique characteristics of Greek court decisions and legal opinions) and the language used.
Most research works do not provide enough details to reproduce the adopted approaches. Especially in the case of rule-based systems (being the majority of automated markup efforts in the literature), the performance highly depends on the completeness of the employed set of rules.
In the Related Work section, we referenced two research projects oriented towards Greek legal documents that can be applied in texts written in the Greek language. In [20], neither the source code of the “Solon” platform nor the set of grammar rules used for legal text parsing are published. On the other hand, while the legal parser presented in [19] is available on Github, it is used to parse documents of the Government Gazette and detect their structure and cannot be used to extract structural elements of court decisions or legal opinions. Moreover, legal references in these documents follow a much stricter drafting style, due to rules set by a special committee, while judges are free to write legal citations according to their personal taste. As a result, a direct comparison of the performance of legal citations’ extraction between the two approaches would not make sense.
We should stress at this point that it was not our primary goal to propose the optimal parsing approach for solving the automated transformation problem. Filling the gap that Sannier et al. underlined in [12], we wanted to validate the hypothesis that automated methodologies can be applied with satisfactory results in the large scale processing of legal documents, highly reducing the need for manual effort. As already discussed, the presented evaluation results successfully confirmed this hypothesis.

10. Conclusions

The legal domain is a domain where large volumes of legal documents are produced; however, usually the publishing bodies do not follow the Open Data paradigm, preventing data sharing and reuse and consequently limiting the potential benefits that could arise. We strongly believe that Legal Open Data publishing should be a responsibility and a priority of each government and we agree with the opinion of Charalabidis et al. who state that “rather than liberating data ex-post, the processes of data generation have to be open by design in order to minimize the cost of making them available to relevant stakeholders” [47]. Nevertheless, until the open-by-design model is adopted by the governments and the legal publishing bodies, we cannot but direct our research efforts on facilitating the ex-post transformation of raw legal documents into Open Data. Moving in this direction, we presented a methodology adapted to the ecosystem approach for the automatic transformation of court decisions and legal opinions into Open Data using legal language processing techniques.
In the future, we plan to extend our grammar, adding more legal texts in the base set used to extract the DSL rules and study how the markup quality is influenced. One should expect the performance to increase, since more language patterns are identified, until reaching a saturation level where adding more texts only slightly improves the results, as Sannier et al. note in [42]. Moreover, in order to attach more accurate semantic information to the legal text, we intend to investigate possible improvements of the applied NER approach or the application of other approaches, such as the one implemented by Angelidis et al. [56] that seems to provide very good results for Greek legal texts. Towards this direction, we are currently working on distinguishing the different roles involved in the judiciary process (e.g., judge, lawyer, defendant, appellant), usually present at the header of the legal documents.
Another direction for further research is the application of the ecosystem approach from the consumer side. As already discussed, data opening does not guarantee data exploitation. To achieve this, we plan to collaborate with the legal community (e.g., lawyers and juries) in order to understand the needs of the involved stakeholders and propose a strategy for the exploitation of available Legal Open Data. Generating value from Open Data is a complex process and requires the collaboration of all involved actors. In [57], we have already identified some usage scenarios that take advantage of legal open datasets and we intend to build on top of this work. Working in this direction, we also want to implement a web platform that would provide enhanced access (employing visualization techniques and providing a REST API) to the generated open dataset.

Author Contributions

Conceptualization, J.G., K.P. and A.P.; Data curation, K.P. and P.S.; Formal analysis, A.P. and P.S.; Funding acquisition, J.G., K.P., A.P. and P.S.; Investigation, P.S.; Methodology, J.G. and A.P.; Project administration, A.P.; Resources, K.P. and P.S.; Software, K.P.; Supervision, J.G.; Validation, K.P., A.P. and P.S.; Writing—original draft, A.P. All authors have read and agreed to the published version of the manuscript.

Funding

The project “Automated Analysis and Processing of Legal Texts for their Transformation into Legal Open Data” is implemented through the Operational Program “Human Resources Development, Education and Lifelong Learning” and is co-financed by the European Union (European Social Fund) and Greek national funds.

Acknowledgments

We would like to thank Haris Papageorgiou and his research team at the Institute for Language & Speech Processing of the “ATHENA” Research Center for applying their Named Entity Recognizer on the legal corpus and extracting the desired entities.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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