Use of linguistic forms mining in the link analysis of legal documents

Abstract

This document employs a statistical approach in exploring language and extracting linguistic forms there contained, so as to identify the linguistic forms which are most frequently used in legal documents. Thus retrieved data, as the second part of this paper shows, can be used to research information, analyze references and links, trace pathways between correlating legal documents and establish the relevance of legal documents on the grounds of their mutual correlation. The retrieved data can further be utilized in various other manners. The methodology of this research and thus attained information form a good basis and act as input data for numerous further analyses.

A Survey of Research on Data Analytics-Based Legal Tech

“…For instance, legal documents and their citation relationships can be represented as nodes and edges of a network. Network analysis techniques can be used to find important nodes or a community of some nodes, which are useful for information visualization and document recommendation [42,43].…”

“…Furthermore, the research papers listed in Table 11 generally provide methodologies and applications for information extraction. The topics of the research papers include entity recognition [43,48,93,99], similarity-score-based recommendation or information retrieval [96,98], information visualization [42], and opinion mining [100], etc.…”

Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach

“…Specifically, we tried retrieving the quantified features, like topic prevalence, temporal alteration and sentiment propensity, and demonstrated the features statistically to provide a full picture of the case. Retrieving information from news texts can be challenging, as it is highly unstructured compared with other semi-structural text sources such as legal documents [ 28 ], patent documents [ 29 ], or electronic health records [ 30 ]. In this case, we used a topic modeling approach to deconstruct the news contents, and a lexicon-based method to analyze the news sentiments.…”

Transfer learning and GRU-CRF augmentation for Covid-19 fake news detection

“…Natural Language Understanding (NLU) is a branch of artificial intelligence (AI) that uses computer software to understand input presented in the text or speech format [13]. NLU is applied in automated reasoning, machine translation, question answering, newsgathering, text categorization, voice-activation, archiving, and large-scale content analysis [14,15,16]. We used Natural Language Understanding to do text categorization because it is more intelligent and efficient, which significantly challenges the semantic understanding in the system's module.…”