Predicting applicable law sections from judicial case reports using legislative text analysis with machine learning

2021 ◽  
Author(s):  
Souvik Sengupta ◽  
Vishwang Dave
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Case Reports ◽  
Applicable Law

Summaries

2018 ◽  
Vol 46 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Text Analysis ◽  
English Language ◽  
Automated Analysis ◽  
Supervised Machine Learning ◽  
Automated Text Analysis ◽  
Research Problems ◽  
Dutch Language ◽  
Automated Methods

Damian Trilling & Jelle Boumans Automated analysis of Dutch language-based texts. An overview and research agenda While automated methods of content analysis are increasingly popular in today’s communication research, these methods have hardly been adopted by communication scholars studying texts in Dutch. This essay offers an overview of the possibilities and current limitations of automated text analysis approaches in the context of the Dutch language. Particularly in dictionary-based approaches, research is far less prolific as research on the English language. We divide the most common types of content-analytical research questions into three categories: 1) research problems for which automated methods ought to be used, 2) research problems for which automated methods could be used, and 3) research problems for which automated methods (currently) cannot be used. Finally, we give suggestions for the advancement of automated text analysis approaches for Dutch texts. Keywords: automated content analysis, Dutch, dictionaries, supervised machine learning, unsupervised machine learning

A content spectral-based text representation

2021 ◽  
pp. 1-12
Author(s):  
Melesio Crespo-Sanchez ◽  
Ivan Lopez-Arevalo ◽  
Edwin Aldana-Bobadilla ◽  
Alejandro Molina-Villegas
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Question Answering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Representation ◽  
Feature Vectors ◽  
Learning Tasks ◽  
Semantic Component ◽  
Vector Representations

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.

Machine learning based text analysis for intelligence collation

2005 ◽  
Author(s):  
C. Thie ◽  
Z. Lock ◽  
D. Smith ◽  
E. Cribb ◽  
A. Ford ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis

scholarly journals The Diversity–Innovation Paradox in Science

2020 ◽  
Vol 117 (17) ◽  
pp. 9284-9291 ◽  
Author(s):  
Bas Hofstra ◽  
Vivek V. Kulkarni ◽  
Sebastian Munoz-Najar Galvez ◽  
Bryan He ◽  
Dan Jurafsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Racial Minorities ◽  
Academic Careers ◽  
Prior Work ◽  
Underrepresented Groups ◽  
Scientific Careers ◽  
Faculty Positions ◽  
Majority Groups ◽  
Scientific Innovations

Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia.

scholarly journals IMPLICIT LOGICAL-SEMANTIC RELATIONS AND A METHOD OF THEIR IDENTIFICATION IN PARALLEL TEXTS

2020 ◽  
Author(s):  
A. A. Goncharov ◽  
◽  
O. Yu. Inkova ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Semantic Relations ◽  
Source Text ◽  
Parallel Corpus ◽  
Source Language ◽  
Original Definition ◽  
Corpus Data ◽  
Parallel Texts ◽  
Logical Semantic

One of the main characteristics of logical-semantic relations (LSRs) between two fragments of a text is that these relations can be either explicit (expressed by some marker, e.g. a connective) or implicit (derived from the interrelation of these fragments’ semantics). Since implicit LSRs do not have any marker, it is difficult to find them in a text (whether automatically or not). In this paper, approaches to analysing implicit LSRs are compared, an original definition for them is offered and differences between implicit LSRs and LSRs expressed by non-prototypical means are described. A method is proposed to identify implicit LSRs using a parallel corpus and a supracorpora database of connectives. Based on the well-known statement that LSRs can be explicitated by adding connectives in the translation, it is argued here that through selecting pairs in which fragments where a connective is used to express an LSR in the translation correspond to those containing any of the translation stimuli standard for this connective in the source language, it is possible to get an array of contexts in which this LSR is implicit in the source text (or expressed by means other than connectives). This method is then applied to study the French causal connectives car, parce que and puisque using a Russian-French parallel corpus. The corpus data are analysed to obtain information about LSRs particularly about cases where the causal LSR in Russian is implicit, as well as about the use of causal connectives in French. These results are used to show that the method proposed allows to quickly create a representative array of contexts with implicit LSRs, which can be useful in both text analysis and in machine learning.

scholarly journals Stemming and Lemmatization of Tweets for Sentiment Analysis using R

2019 ◽  
Vol 8 (2) ◽  
pp. 2038-2040
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Analysis ◽  
Rapid Rate ◽  
Volume Data ◽  
Huge Amount ◽  
Manual Analysis ◽  
User Communities ◽  
Use Of Social Media ◽  
Day By Day

In our digital India, the use of social media like twitter, blogs and various forums is growing with the rapid rate. Thus the size of the data is becoming big day by day and in the span of this type of high varied and volume data, manual analysis would be a clumsy job. So, there is an alarming rate to analyze that large amount of data to make it suitable for analysis purpose. As a most elaborate open source platform, R has immeasurable user communities that thrives and perpetuate a huge amount of text analysis packages. So, in this paper we are analyzing movie related tweets using machine learning in R.

Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

2019 ◽  
pp. 1-50
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda ◽  
Nina Rizun ◽  
Vitaliy Lazorenko ◽  
Volodymyr Kharchenko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Analysis ◽  
Intelligent System ◽  
Air Traffic ◽  
Civil Aviation ◽  
Air Traffic Controllers ◽  
The Neural Network ◽  
University Of Technology ◽  
New Information

This chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype of the neural network for evaluating the timeliness and correctness of the decision making by ATCs has been developed. The new theoretical and practical tasks for simulation and pre-simulation training have been obtained using expert judgment method. The methodology for sentiment analyzing the airline customers' opinions has been proposed. In addition, the examples of artificial intelligence systems and expert systems by the authors, students and colleagues from National Aviation University, Ukraine and Gdansk University of Technology, Poland have been proposed.

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