2,404 research outputs found
Question Answering with Subgraph Embeddings
This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature
A Robust Deep Model for Improved Categorization of Legal Documents for Predictive Analytics
Predictive legal analytics is a technology used to predict the chances of successful and unsuccessful outcomes in a particular case. Predictive legal analytics is performed through automated document classification for facilitating legal experts in their classification of court documents to retrieve and understand the details of specific legal factors from legal judgments for accurate document analysis. However, extracting these factors from legal texts document is a time-consuming process. In order to facilitate the task of classifying documents, a robust method namely Distributed Stochastic Keyword Extraction based Ensemble Theil-Sen Regressive Deep Belief Reweight Boost Classification (DSKE-TRDBRBC) is proposed. The DSKE-TRDBRBC technique consists of two major processes namely Keyword Extraction and Classification. At first, the t-distributed stochastic neighbor embedding technique is applied to DSKE-TRDBRBC for keyword extraction. This in turn minimizes the time consumption for document classification. After that, the Ensemble Theil-Sen Regressive Deep Belief Reweight Boosting technique is applied for document classification. The Ensemble boosting algorithm initially constructs’ set of Theil-Sen Regressive Deep Belief neural networks to classify the input legal documents. Then the results of the Deep Belief neural network are combined to built a strong classifier by reducing the error. This aids in improving the classification accuracy. The proposed method is experimentally evaluated with various metrics such as F-measure , recall, accuracy, precision, , and computational time. The experimental results quantitatively confirm that the proposed DSKE-TRDBRBC technique achieves better accuracy with lowest computation time as compared to the conventional approaches
Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives
The automated identification of national implementations (NIMs) of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (from Ireland, Luxembourg and Italy) to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets
Using NLP to Model U.S. Supreme Court Cases
The advantages of employing text analysis to uncover policy positions, generate legal predictions, and inform or evaluate reform practices are multifold. Given the far-reaching effects of legislation at all levels of society these insights and their continued improvement are impactful. This research explores the use of natural language processing (NLP) and machine learning to predictively model U.S. Supreme Court case outcomes based on textual case facts. The final model achieved an F1-score of .324 and an AUC of .68. This suggests that the model can distinguish between the two target classes; however, further research is needed before machine learning models are used in the Supreme Court
ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models
Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on
case description. Several researchers have developed techniques to assist
potential clients by predicting the outcome in the legal profession. However,
none of the proposed techniques were implemented in Arabic, and only a few
attempts were implemented in English, Chinese, and Hindi. In this paper, we
develop a system that utilizes deep learning (DL) and natural language
processing (NLP) techniques to predict the judgment outcome from Arabic case
scripts, especially in cases of custody and annulment of marriage. This system
will assist judges and attorneys in improving their work and time efficiency
while reducing sentencing disparity. In addition, it will help litigants,
lawyers, and law students analyze the probable outcomes of any given case
before trial. We use a different machine and deep learning models such as
Support Vector Machine (SVM), Logistic regression (LR), Long Short Term Memory
(LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) using representation
techniques such as TF-IDF and word2vec on the developed dataset. Experimental
results demonstrate that compared with the five baseline methods, the SVM model
with word2vec and LR with TF-IDF achieve the highest accuracy of 88% and 78% in
predicting the judgment on custody cases and annulment of marriage,
respectively. Furthermore, the LR and SVM with word2vec and BiLSTM model with
TF-IDF achieved the highest accuracy of 88% and 69% in predicting the
probability of outcomes on custody cases and annulment of marriage,
respectively
Using attention methods to predict judicial outcomes
Legal Judgment Prediction is one of the most acclaimed fields for the
combined area of NLP, AI, and Law. By legal prediction we mean an intelligent
systems capable to predict specific judicial characteristics, such as judicial
outcome, a judicial class, predict an specific case. In this research, we have
used AI classifiers to predict judicial outcomes in the Brazilian legal system.
For this purpose, we developed a text crawler to extract data from the official
Brazilian electronic legal systems. These texts formed a dataset of
second-degree murder and active corruption cases. We applied different
classifiers, such as Support Vector Machines and Neural Networks, to predict
judicial outcomes by analyzing textual features from the dataset. Our research
showed that Regression Trees, Gated Recurring Units and Hierarchical Attention
Networks presented higher metrics for different subsets. As a final goal, we
explored the weights of one of the algorithms, the Hierarchical Attention
Networks, to find a sample of the most important words used to absolve or
convict defendants
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