A Novel Approach of Augmenting Training Data for Legal Text Segmentation by Leveraging Domain Knowledge

2019 ◽  
pp. 53-63 ◽  
Author(s):  
Rupali Sunil Wagh ◽  
Deepa Anand
Keyword(s):  
Domain Knowledge ◽  
Training Data ◽  
Text Segmentation ◽  
Legal Text ◽  
Novel Approach

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

scholarly journals Removal of Cochannel Interference Using Probabilistic Latent Component Analysis in Passive Bistatic Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tao Ying ◽  
Xuebao Wang ◽  
Wei Tian ◽  
Cheng Zhou
Keyword(s):  
Target Detection ◽  
Reference Signal ◽  
Component Analysis ◽  
Cochannel Interference ◽  
Training Data ◽  
Bistatic Radar ◽  
Frequency Channel ◽  
Novel Approach ◽  
Passive Bistatic Radar

This paper examines the problem of cancellation of cochannel interference (CCI) present in the same frequency channel as the signal of interest, which may bring a reduction in the performance of target detection, in passive bistatic radar. We propose a novel approach based on probabilistic latent component analysis for CCI removal. The highlight is that removing CCI is considered as reconstruction, and extraction of Doppler-shifted and time-delayed replicas of the reference signal exploited fully as training data. The results of the simulation show that the developed method is effective.

scholarly journals Discovering Latent Class Labels for Multi-Label Learning

2020 ◽  
Author(s):  
Jun Huang ◽  
Linchuan Xu ◽  
Jing Wang ◽  
Lei Feng ◽  
Kenji Yamanishi
Keyword(s):  
Large Scale ◽  
Latent Class ◽  
Training Data ◽  
Data Sets ◽  
Robust Learning ◽  
Large Scale Data ◽  
Novel Approach ◽  
Fixed Set ◽  
Class Labels ◽  
Scale Data

Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously. Extensive experiments show a competitive performance of DLCL against other state-of-the-art MLL approaches.

Prediction of Gas Turbine Trip: a Novel Methodology Based on Random Forest Models

2021 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Remaining Useful Life ◽  
Training Data ◽  
The Novel ◽  
Novel Approach ◽  
Forest Models ◽  
Random Forest Models

Abstract A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, Random Forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops Random Forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case-studies, involving filed data taken during three years of operation of two fleets of Siemens gas turbines located in different regions. The novel methodology allows values of Precision, Recall and Accuracy in the range 75–85 %, thus demonstrating the industrial feasibility of the predictive methodology.

Chapter 8. A Constraint-Based Approach to Learning and Reasoning

2021 ◽  
Author(s):  
Michelangelo Diligenti ◽  
Francesco Giannini ◽  
Marco Gori ◽  
Marco Maggini ◽  
Giuseppe Marra
Keyword(s):  
Neural Networks ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Graphical Model ◽  
Expressive Power ◽  
Training Data ◽  
Markov Logic ◽  
Exact Inference ◽  
Logic Models ◽  
Effectiveness Of Training

Neural-symbolic models bridge the gap between sub-symbolic and symbolic approaches, both of which have significant limitations. Sub-symbolic approaches, like neural networks, require a large amount of labeled data to be successful, whereas symbolic approaches, like logic reasoners, require a small amount of prior domain knowledge but do not easily scale to large collections of data. This chapter presents a general approach to integrate learning and reasoning that is based on the translation of the available prior knowledge into an undirected graphical model. Potentials on the graphical model are designed to accommodate dependencies among random variables by means of a set of trainable functions, like those computed by neural networks. The resulting neural-symbolic framework can effectively leverage the training data, when available, while exploiting high-level logic reasoning in a certain domain of discourse. Although exact inference is intractable within this model, different tractable models can be derived by making different assumptions. In particular, three models are presented in this chapter: Semantic-Based Regularization, Deep Logic Models and Relational Neural Machines. Semantic-Based Regularization is a scalable neural-symbolic model, that does not adapt the parameters of the reasoner, under the assumption that the provided prior knowledge is correct and must be exactly satisfied. Deep Logic Models preserve the scalability of Semantic-Based Regularization, while providing a flexible exploitation of logic knowledge by co-training the parameters of the reasoner during the learning procedure. Finally, Relational Neural Machines provide the fundamental advantages of perfectly replicating the effectiveness of training from supervised data of standard deep architectures, and of preserving the same generality and expressive power of Markov Logic Networks, when considering pure reasoning on symbolic data. The bonding between learning and reasoning is very general as any (deep) learner can be adopted, and any output structure expressed via First-Order Logic can be integrated. However, exact inference within a Relational Neural Machine is still intractable, and different factorizations are discussed to increase the scalability of the approach.

scholarly journals Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition

2020 ◽  
Vol 34 (03) ◽  
pp. 2645-2652 ◽  
Author(s):  
Yaman Kumar ◽  
Dhruva Sahrawat ◽  
Shubham Maheshwari ◽  
Debanjan Mahata ◽  
Amanda Stent ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Classification Problem ◽  
Visual Speech ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Visual Speech Recognition ◽  
Training Samples ◽  
English Training

Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data leads to error-prone systems with very low accuracies in predicting unseen classes. To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. We also show that our models are language agnostic and therefore capable of seamlessly generating, using English training data, videos for a new language (Hindi). To the best of our knowledge, this is the first work to show empirical evidence of the use of GANs for generating training samples of unseen classes in the domain of VSR, hence facilitating zero-shot learning. We make the added videos for new classes publicly available along with our code1.

scholarly journals Computational construction grammar for visual question answering

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Jens Nevens ◽  
Paul Van Eecke ◽  
Katrien Beuls
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Semantic Representation ◽  
Construction Grammar ◽  
Training Data ◽  
Knowledge Sources ◽  
Visual Question Answering ◽  
Novel Approach ◽  
Natural Language Question ◽  
Grammar Model

AbstractIn order to be able to answer a natural language question, a computational system needs three main capabilities. First, the system needs to be able to analyze the question into a structured query, revealing its component parts and how these are combined. Second, it needs to have access to relevant knowledge sources, such as databases, texts or images. Third, it needs to be able to execute the query on these knowledge sources. This paper focuses on the first capability, presenting a novel approach to semantically parsing questions expressed in natural language. The method makes use of a computational construction grammar model for mapping questions onto their executable semantic representations. We demonstrate and evaluate the methodology on the CLEVR visual question answering benchmark task. Our system achieves a 100% accuracy, effectively solving the language understanding part of the benchmark task. Additionally, we demonstrate how this solution can be embedded in a full visual question answering system, in which a question is answered by executing its semantic representation on an image. The main advantages of the approach include (i) its transparent and interpretable properties, (ii) its extensibility, and (iii) the fact that the method does not rely on any annotated training data.

Clustering Approaches for Top-k Recommender Systems

2019 ◽  
Vol 28 (05) ◽  
pp. 1950019 ◽  
Author(s):  
Nicolás Torres ◽  
Marcelo Mendoza
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Large Scale ◽  
Empirical Studies ◽  
Cluster Structure ◽  
Parallel Architecture ◽  
Evaluation Framework ◽  
Training Data ◽  
Cluster Solution ◽  
Novel Approach

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.

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