Text mining tools in legal documents

We present the architecture of the system for the intellectual textual analysis in jurisprudence based on microservices. The system can identify common dependencies on an existing database of legal documents, provide legal cases close to each other, familiarize them with the most probable outcomes of judicial review or mark out important places during procedural actions

Automated Attribute Extraction from Legal Proceedings

The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. We use a state-of-the-art sequence labeling framework to automatically extract attributes from the legal documents. Moreover, we demonstrate the efficacy of the extracted attributes in a downstream task, namely legal judgment prediction.Comment: Presented in Mining and Learning in the Legal Domain (MLLD) workshop 202

Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

Extracting and re-using research data from chemistry e-theses: the SPECTRa-T project

Scientific e-theses are data-rich resources, but much of the information they contain is not readily accessible. For chemistry, the SPECTRa-T project has addressed this problem by developing data-mining techniques to extract experimental data, creating RDF (Resource Description Framework) triples for exposure to sophisticated Semantic Web searches. We used OSCAR3, an Open Source chemistry text-mining tool, to parse and extract data from theses in PDF, and from theses in Office Open XML document format. Theses in PDF suffered data corruption and a loss of formatting that prevented the identification of chemical objects. Theses in .docx yielded semantically rich SciXML that enabled the additional extraction of associated data. Chemical objects were placed in a data repository, and RDF triples deposited in a triplestore. Data-mining from chemistry e-theses is both desirable and feasible; but the use of PDF, the de facto format standard for deposit in most repositories, prevents the optimal extraction of data for semantic querying. In order to facilitate this, we recommend that universities also require deposition of chemistry e-theses in an XML document format. Further work is required to clarify the complex IPR issues and ensure that they do not become an unwarranted barrier to data extraction and re-use

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems