scholarly journals Natural Language Processing for Lawyers and Judges

2021 ◽  
pp. 1399
Author(s):  
Frank Fagan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Legal Analysis ◽  
The Future

A Review of Law as Data: Computation, Text, & the Future of Legal Analysis. Edited by Michael A. Livermore and Daniel N. Rockmore.

Automated Writing Assistance

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Writing Process ◽  
Modern Society ◽  
The Future ◽  
The 1960S ◽  
Processing Techniques

The ability to communicate in writing is an essential skill in modern society. But ability in writing varies considerably; and no matter what their existing level of competence, most writers would acknowledge that what they write could often be improved. Given that the output of the writing process is natural language, it seems plausible that natural language processing techniques might be used to analyse this output and to suggest ways to improve it. In various guises, this has indeed been an application of NLP at least since the 1960s. In this chapter, we survey the different kinds of assistance to authors that NLP makes possible; we describe what can be done today, and explore what might be possible in the future.

scholarly journals Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

2020 ◽  
Vol 12 (19) ◽  
pp. 7848 ◽  
Author(s):  
Israel Griol-Barres ◽  
Sergio Milla ◽  
Antonio Cebrián ◽  
Huaan Fan ◽  
Jose Millet
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Weak Signals ◽  
Remote Sensors ◽  
The Future ◽  
Increasing Demand ◽  
New Applications ◽  
Start Ups

Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

Automated Writing Assistance

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Writing Process ◽  
Modern Society ◽  
The Future ◽  
The 1960S ◽  
Processing Techniques

The ability to communicate in writing is an essential skill in modern society. But ability in writing varies considerably; and no matter what their existing level of competence, most writers would acknowledge that what they write could often be improved. Given that the output of the writing process is natural language, it seems plausible that natural language processing techniques might be used to analyse this output and to suggest ways to improve it. In various guises, this has indeed been an application of NLP at least since the 1960s. In this chapter, we survey the different kinds of assistance to authors that NLP makes possible; we describe what can be done today, and explore what might be possible in the future.

scholarly journals Foundational ontologies meet ontology matching: A survey

Semantic Web ◽  
2021 ◽  
pp. 1-20
Author(s):  
Cassia Trojahn ◽  
Renata Vieira ◽  
Daniela Schmidt ◽  
Adam Pease ◽  
Giancarlo Guizzardi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reference Model ◽  
Research Area ◽  
Ontology Matching ◽  
The Future

Ontology matching is a research area aimed at finding ways to make different ontologies interoperable. Solutions to the problem have been proposed from different disciplines, including databases, natural language processing, and machine learning. The role of foundational ontologies for ontology matching is an important one, as they provide a well-founded reference model that can be shared across domains. It is multifaceted and with room for development. This paper presents an overview of the different tasks involved in ontology matching that consider foundational ontologies. We discuss the strengths and weaknesses of existing proposals and highlight the challenges to be addressed in the future.

scholarly journals Desktop Voice Assistant Using Natural Language Processing (NLP)

2020 ◽  
Vol 6 (12) ◽  
pp. 332-335
Author(s):  
Lalit Kumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cloud Services ◽  
Home Automation ◽  
The People ◽  
The Future ◽  
The Voice ◽  
The Way

Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. the voice assistant was first introduced on smartphones and after the popularity it got. It was widely accepted by all. Initially, the voice assistant was mostly being used in smartphones and laptops but now it is also coming as home automation and smart speakers. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.

scholarly journals The Limits of Annotation in Machine Learning a Documents Hohfeldian Legal Entities

2021 ◽  
Author(s):  
Ahmed Izzidien
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Essential Elements ◽  
Legal Analysis ◽  
Domain Specific ◽  
Legal Relation ◽  
Alternative Approach ◽  
Legal Entities

Natural language processing (NLP) summarisers aim to capture the essential elements of a document. Yet, the ontological character of a summary can be domain specific. In legal analysis, the Hohfeldian matrix is used to summarise principle legal relations between agents, such as individuals and organisations. We test a limit of using machine learning (ML) to detect such agents. Based on training with our 2400 hand labelled annotations, an F1= 80.1 is found. Extrapolating this suggests that over one million annotations are required to capture all the agents mentioned in a document. This questions the feasibility of such an approach, one that is unable to be inclusive of all agents who are party to a legal relation. Such complete capture is an essential criteria of fair ML and accurate legal summaries. An alternative approach based on hypernymy is suggested.

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