Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> Historically, story understanding systems have depended on a great deal of hand-crafted knowledge. Natural language understanding systems that use conceptual knowledge structures [SA77, Cul78, Wil78, Car79, Leh81, Kol83] typically rely on enormous amounts of manual knowledge engineering. While much of the work on conceptual knowledge structures has been hailed as pioneering research in cognitive modeling and narrative understanding, from a practical perspective it has also been viewed with skepticism because of the underlying knowledge engineering bottleneck. The thought of building a large-scale conceptual natural language processing (NLP) system that can understand open-ended text is daunting even to the most ardent enthusiasts. So must we grit our collective teeth and assume that story understanding will be limited to prototype systems in the foreseeable future? Or will conceptual natural language processing ultimately depend on a massive, broad-scale manual knowledge engineering effort, such as CYC [LPS86]? <s> BIB001 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> The prohibitive cost of assigning indices to textual cases is a major obstacle for the practical use of AI and Law systems supporting reasoning and arguing with cases. While progress has been made toward extracting certain facts from well-structured case texts or classifying case abstracts under Key Number concepts, these methods still do not suffice for the complexity of indexing concepts in CBR systems. In this paper, we lay out how a better example representation may facilitate classification-based indexing. Our hypotheses are that (1) abstracting from the individual actors and events in cases, (2) capturing actions in multi-word features, and (3) recognizing negation, can lead to a better representation of legal case texts for automatic indexing. We discuss how to implement these techniques with state-of-the-art NLP tools. Preliminary experimental results suggest that a combination of domain-specific knowledge and information extraction techniques can be used to generalize from the examples and derive more powerful features. <s> BIB002 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> Intelligent text information retrieval systems need the capability to deal with the semantics of the content of their text bases. In order to satisfy this requisite it is necessary to extract semantic information from the documents and to be able to make inferences about it. A methodology to semi-automatically transform a traditional web IR system into a semantic aware one is proposed. The methodology is composed by three major steps: construction of an appropriate semantic ontology; text enrichment with semantic in- formation; and construction of the inference engine. In order to create an adequate ontology, natural language processing techniques are applied, such as, partial parsers and lexical information (WordNet). Documents are enriched with semantic informa- tion using the output of the partial parsers and the obtained ontology. Finally, an infer- ence engine based on a declarative programming language - Prolog - is used as the basis for the reasoning process. An application of this methodology to the legal web information retrieval system of the Portuguese Attorney General's Office is described. <s> BIB003 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> In this paper, we present a workbench for semi-automatic ontology population from textual documents. It provides an environment for mapping the linguistic extractions with the domain ontology thanks to knowledge acquisition rules. Those rules are activated when a pertinent linguistic tag is reached. Those linguistic tags are then mapped to a concept, one of its attributes or even a semantic relation between several concepts. The rules instantiate these concepts, attributes and relations in the knowledge base constrained by the domain ontology. This paper deals with the underlying knowledge capture process and presents the first experiments realized on a real client application from the legal publishing domain. <s> BIB004 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> Normative texts can be viewed as composed by formal partitions (articles, paragraphs, etc.) or by semantic units containing fragments of a regulation (provisions). Provisions can be described according to a metadata scheme which consists of provision types and their arguments. This semantic annotation of a normative text can make the retrieval of norms easier. The detection and description of the provisions according to the established metadata scheme is an analytic intellectual activity aiming at classifying portions of a normative text into provision types and to extract their arguments. Automatic facilities supporting this intellectual activity are desirable. Particularly, in this paper, two modules able to qualify fragments of a normative text in terms of provision types and to extract their arguments are presented. <s> BIB005 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> One of the main emerging research challenge in the legal documentation is to penetrate in the meaningful and in the semantic of the norm content using NLP techniques and isolate relevant part of the linguistic speech. This paper wants present a methodology for modeling the modificatory provisions in deep in order to provide all the necessary formalization for managing semi-automatically the consolidation process. <s> BIB006 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> Information extraction (IE) aims to retrieve certain types of information from natural language text by processing them automatically. For example, an IE system might retrieve information about geopolitical indicators of countries from a set of web pages while ignoring other types of information. Ontology-based information extraction (OBIE) has recently emerged as a subfield of information extraction. Here, ontologies - which provide formal and explicit specifications of conceptualizations - play a crucial role in the IE process. Because of the use of ontologies, this field is related to knowledge representation and has the potential to assist the development of the Semantic Web. In this paper, we provide an introduction to ontology-based information extraction and review the details of different OBIE systems developed so far. We attempt to identify a common architecture among these systems and classify them based on different factors, which leads to a better understanding on their operation. We also discuss the implementation details of these systems including the tools used by them and the metrics used to measure their performance. In addition, we attempt to identify the possible future directions for this field. <s> BIB007 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Introduction <s> Thank you very much for reading mining the web discovering knowledge from hypertext data. As you may know, people have look hundreds times for their chosen novels like this mining the web discovering knowledge from hypertext data, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. <s> BIB008
In this work we provide an introduction to the Information Extraction (IE) systems and describe specific needs and possibilities for the legal area. In general, IE can be described as a process aimed at making the recognition and the extraction of certain types of information, as well as the recognition of relevant relations among them  . One important aspect in IE systems is the kind of data manipulated, which remains in the natural language text group BIB001 BIB007 . As an example of an IE system devoted to the legal area, we can describe a system that processes a group of legal documents and extracts information regarding the relevant events, stated facts, individual references and date or time indicators. As a general approach, the IE systems are dependent on specific models that are determinant to guide its correct operation. Known models apply several approaches, such as extraction rules BIB002  BIB008 , linguistic analysis BIB003 BIB004 , ontology or semantic information 16. Some factors are important for the correct law enforcement, such as the knowledge of previous cases, relevant laws and correlated facts. The correct law application presents great difficulties when there is no adequate access to appropriate information and knowledge regarding the subject. Professionals in the legal area need to make exhaustive information search in order to take the correct decisions. That situation is related to the information overload. Various specific systems for processing legal information have been developed in order to fulfill these needs. Therefore, it can be observed the increasing interest in IE systems. There is a close relation between IE systems and Information Retrieval (IR) Systems, in the sense that the results obtained from IE processes can also be incorporated in IR systems, in order to increase precision and expand recovery patterns. This relation can be also observed in different kinds of systems, such as text understanding systems or case based reasoning systems. Systems that deal with legal knowledge are designed to accomplish specific necessities that can be very diverse. However, the IE results can be the most valuable to several cases. An initial example is the information retrieval, from which the user can find laws and case studies relevant to a trial, related to facts that describe a legal situation. Another example is legal reasoning and argumentation, where software systems can help to develop, enhance or identify a useful legal reasoning based on previous cases. Finally, we can mention the edition of legal texts, in which software systems provide support in the production of legal texts. Regardless of the specific area of action and objective, the success of legal information processing systems depends crucially on its legal knowledge representation BIB007  . For instance, in IE systems, the aspects of knowledge representation available are determinant to the precision and recall of the results. Currently, there are several initiatives underway to achieve standards to the representation of legal documents, therefore facilitating their automatic processing. In Brazil, the LEXML 3 project works towards this goal, as well as other projects in Europe, or in other contexts BIB006 BIB005 . In general, standards and schemes are used and implemented in document sets in flexible formats, such as XML, fostering the generation of various patterns for document annotation and the access initiatives for automatic processing. This trend in providing affordable computational formats is an important fact showing the correct positioning of the efforts to create automatic tools for the treatment of legal documents. This work presents an overview of Information Extraction approaches and an analysis regarding its main characteristics and limitations. Due to the close relation between IE systems and IR systems, the work also presents a general overview of the IR process and some specific aspects of existing IR systems. Techniques that rely on the representation of legal knowledge are discussed in particular attention in order to achieve better results, with emphasis on techniques that explore ontologies aspects and linguistic support. Some details of the implementations are also presented, followed by an analysis of the positive and negative aspects of each approach, aiming at bringing to the reader a critical position regarding the techniques and the solutions available. The text is organized as follows. In section 2, proposals of Information Retrieval Systems are reviewed, giving particular attention to those regarding the legal field. In section 3, the systems that use computational representation of legal knowledge are described, focusing on those that use ontologies. From this analysis, some issues and limitations inherent to the conceptual model of knowledge representation used in each examined solution are highlighted. In section 4, the details of the information extraction process in the analyzed systems are presented, clustering them by the applied technique. Section 5 is dedicated to the review and discussion regarding analyzed systems features. Finally, in section 6, present the conclusions of this work are presented.