Information extraction for legal knowledge representation – a review of approaches and trends <s> Keyword indexing IR systems examples in legal domain <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> BIB001 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Keyword indexing IR systems examples in legal domain <s> Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods. ::: ::: Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval. <s> BIB002 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Keyword indexing IR systems examples in legal domain <s> This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness. <s> BIB003 </s> Information extraction for legal knowledge representation – a review of approaches and trends <s> Keyword indexing IR systems examples in legal domain <s> Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works. <s> BIB004
There are several examples of both public and private IR systems that present solutions based on words indexing. We do not intend to present a complete list in this paper but suggest related work on the subject   BIB002 . Nevertheless, some of these IR systems aimed at the legal documents manipulation are briefly commented below. This list is not complete, but it presents some highly accessed web portals in the legal field, in order to provide some evidences of the IR systems widely adopted in this area, as well as an initial step to analyze some of its weaknesses. The first portal BIB001 to be analyzed is LexML Web, which was officially released in June, 2009. It can be considered the main outcome of the project Brazil LexML-BR. This portal is specialized in legal and legislative Brazilian information. It aims at unifying, organizing and facilitating access to descriptive information, case law, legislation and doctrine of legislative propositions in public administration. The LexML is one of the key pieces of the federal Brazilian Electronic Government program  . Another example, the Web portal JusBrasil 6 is the most accessed Brazilian legal site, as stated in a research made by the Website Alexa 7 audience meter. This is a private web portal for hosting news, legislation and case law. Finally, the web portal FindLaw 8 is one of the largest legal information portals in the world. Founded in 1996, it provides access to information on legal cases and statutes, among other services. The portal is still today one of the most popular free legal information websites on the Internet, with more than four million hits per month. Users of traditional IR systems, as the ones just cited above, have in common the fact that they must always think of your searches in terms of key words. This is not the most suitable way for information search and is considered as a negative aspect of index-based IR model 17. In addition, questions concerning the ambiguity and semantics of words are not properly treated in these systems, implying less accurate results BIB003  BIB004 . It can be noted that in legal domain, the known problems of IR systems based on word indexing appear in the same manner than in another field. For instance, the problems in ranking a great number of documents obtained from a query or the word and term ambiguity have both the same origin. One important difference that can be noted, however, is related to the possible consequences of these problems. When a word indexing based IR system excludes from the results some relevant documents to a given query, the possible consequence is the poor law enforcement or a lower quality appeal, due to the lack of relevant jurisprudence. Besides that, the structured and concept-related nature of the legal documents represents an important aspect that carries the possibility of better indexing techniques application. Smarter recovery systems should make use of appropriate language for knowledge representation in order to obtain better and more accurate results. Some of these limitations identified in word indexing IR systems are overcome with intelligent systems for localization and recovery of legal information, which will be presented in the subsequent section.