scholarly journals Legal Information Retrieval Using Topic Clustering and Neural Networks

10.29007/psgx ◽  
2018 ◽  
Author(s):  
Rohan Nanda ◽  
Adebayo Kolawole John ◽  
Luigi Di Caro ◽  
Guido Boella ◽  
Livio Robaldo
Keyword(s):  
Information Retrieval ◽  
Short Term Memory ◽  
String Matching ◽  
Clustering Method ◽  
Short Term ◽  
Retrieval Task ◽  
Legal Information ◽  
Textual Entailment ◽  
Topic Clustering ◽  
Long Short Term Memory

This paper presents a description about our adopted approach for the information retrieval and textual entailment tasks of the COLIEE 2017 competition. We address the information retrieval task by implementing a partial string matching and a topic clustering method. For the textual entailment task, we propose a Long Short-Term Memory (LSTM) - Convolutional Neural Network (CNN) model which utilizes word embeddings trained on the Google News vectors. We evaluated our approach for both tasks on the COLIEE 2017 dataset. The results demonstrate that the topic clustering method outperformed the partial string matching method in the information retrieval task. The performance of LSTM-CNN model was competitive with other textual entailment systems.

Maintaining Recency despite a Stimulus Suffix

1975 ◽  
Vol 27 (3) ◽  
pp. 433-443 ◽  
Author(s):  
David Salter
Keyword(s):  
Information Retrieval ◽  
Short Term Memory ◽  
Cognitive System ◽  
List Presentation ◽  
Short Term ◽  
Retrieval Task ◽  
Term Memory ◽  
Focal Attention ◽  
Final Item ◽  
Physical Features

Two processes of information retrieval were considered in the context of the logogen model. The aim was to establish whether information about the final items of an auditory short-term memory list is held exclusively in precategorical acoustic storage at presentation or whether these items are automatically registered in a cognitive store as well. Error data for a final heterogeneous item in alphanumeric lists showed significantly better recall, despite the addition of a stimulus suffix. Although these results demonstrated that coding had proceeded further than a precategorical stage, which maintains only physical features, the possibility remained that the effect was due to a bias in focal attention and selective coding at list presentation. A second experiment increased the difficulty of the retrieval task, and effectively precluded the possibility of a bias in attention. The results confirmed the findings in the first experiment. It was concluded that information about the final item(s) is registered automatically in the cognitive system, and that responses are made available from this source when information about physical features of the item is degraded by a stimulus suffix.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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