Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Μηχανική Μάθηση στην Επεξεργασία Φυσικής Γλώσσας

Η διατριβή εξετάζει την χρήση τεχνικών μηχανικής μάθησης σε διάφορα στάδια της επεξεργασίας φυσικής γλώσσας, κυρίως για σκοπούς εξαγωγής πληροφορίας από κείμενα. Στόχος είναι τόσο η βελτίωση της προσαρμοστικότητας των συστημάτων εξαγωγής πληροφορίας σε νέες θεματικές περιοχές (ή ακόμα και γλώσσες), όσο και η επίτευξη καλύτερης απόδοσης χρησιμοποιώντας όσο το δυνατό λιγότερους πόρους (τόσο γλωσσικούς όσο και ανθρώπινους). Η διατριβή κινείται σε δύο κύριους άξονες: α) την έρευνα και αποτίμηση υπαρχόντων αλγορίθμων μηχανικής μάθησης κυρίως στα στάδια της προ-επεξεργασίας (όπως η αναγνώριση μερών του λόγου) και της αναγνώρισης ονομάτων οντοτήτων, και β) τη δημιουργία ενός νέου αλγορίθμου μηχανικής μάθησης και αποτίμησής του, τόσο σε συνθετικά δεδομένα, όσο και σε πραγματικά δεδομένα από το στάδιο της εξαγωγής σχέσεων μεταξύ ονομάτων οντοτήτων. Ο νέος αλγόριθμος μηχανικής μάθησης ανήκει στην κατηγορία της επαγωγικής εξαγωγής γραμματικών, και εξάγει γραμματικές ανεξάρτητες από τα συμφραζόμενα χρησιμοποιώντας μόνο θετικά παραδείγματα.This thesis examines the use of machine learning techniques in various tasks of natural language processing, mainly for the task of information extraction from texts. The objectives are the improvement of adaptability of information extraction systems to new thematic domains (or even languages), and the improvement of their performance using as fewer resources (either linguistic or human) as possible. This thesis has examined two main axes: a) the research and assessment of existing algorithms of machine learning mainly in the stages of linguistic pre-processing (such as part of speech tagging) and named-entity recognition, and b) the creation of a new machine learning algorithm and its assessment on synthetic data, as well as in real world data from the task of relation extraction between named entities. This new algorithm belongs to the category of inductive grammar learning, and can infer context free grammars from positive examples only

Learning with Minimal Supervision: New Meta-Learning and Reinforcement Learning Algorithms

Standard machine learning approaches thrive on learning from huge amounts of labeled training data, but what if we don’t have access to large amounts of labeled datasets? Humans have a remarkable ability to learn from only a few examples. To do so, they either build upon their prior learning experiences, or adapt to new circumstances by observing sparse learning signals. In this dissertation, we promote algorithms that learn with minimal amounts of supervision inspired by these two ideas. We discuss two families for minimally supervised learning algorithms based on meta-learning (or learning to learn) and reinforcement learning approaches.In the first part of the dissertation, we discuss meta-learning approaches for learning with minimal supervision. We present three meta-learning algorithms for few-shot adaptation of neural machine translation systems, promoting fairness in learned models by learning to actively learn under fairness parity constraints, and learning better exploration policies in the interactive contextual bandit setting. All of these algorithms simulate settings in which the agent has access to only a few labeled samples. Based on these simulations, the agent learns how to solve future learning tasks with minimal supervision. In the second part of the dissertation, we present learning algorithms based on reinforcement and imitation learning. In many settings the learning agent doesn’t have access to fully supervised training data, however, it might be able to leverage access to a sparse reward signal, or an expert that can be queried to collect the labeled data. It is important then to utilize these learning signals efficiently. Towards achieving this goal, we present three learning algorithms for learning from very sparse reward signals, leveraging access to noisy guidance, and solving structured prediction learning tasks under bandit feedback. In all cases, the result is a minimally supervised learning algorithm that can effectively learn given access to sparse reward signals

Text readability and summarisation for non-native reading comprehension

This thesis focuses on two important aspects of non-native reading comprehension: text readability assessment, which estimates the reading difficulty of a given text for L2 learners, and learner summarisation assessment, which evaluates the quality of learner summaries to assess their reading comprehension. We approach both tasks as supervised machine learning problems and present automated assessment systems that achieve state-of-the-art performance. We first address the task of text readability assessment for L2 learners. One of the major challenges for a data-driven approach to text readability assessment is the lack of significantly-sized level-annotated data aimed at L2 learners. We present a dataset of CEFR-graded texts tailored for L2 learners and look into a range of linguistic features affecting text readability. We compare the text readability measures for native and L2 learners and explore methods that make use of the more plentiful data aimed at native readers to help improve L2 readability assessment. We then present a summarisation task for evaluating non-native reading comprehension and demonstrate an automated summarisation assessment system aimed at evaluating the quality of learner summaries. We propose three novel machine learning approaches to assessing learner summaries. In the first approach, we examine using several NLP techniques to extract features to measure the content similarity between the reading passage and the summary. In the second approach, we calculate a similarity matrix and apply a convolutional neural network (CNN) model to assess the summary quality using the similarity matrix. In the third approach, we build an end-to-end summarisation assessment model using recurrent neural networks (RNNs). Further, we combine the three approaches to a single system using a parallel ensemble modelling technique. We show that our models outperform traditional approaches that rely on exact word match on the task and that our best model produces quality assessments close to professional examiners

Using Text Mining to Identify Crime Patterns from Arabic Crime News Report Corpus

Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied at all. In fact, Arabic is a language with a very complex morphology because it is highly inflectional l, and therefore, dealing with texts written in Arabic is highly complicated. This research studies the crime domain in the Arabic language, exploiting unstructured text using text mining techniques. Developing a system for extracting important information from crime reports would be useful for police investigators, for accelerating the investigative process (instead of reading entire reports) as well as for conducting further or wider analyses. We propose the Crime Profiling System (CPS) to extract crime-related information (crime type, crime location and nationality of persons involved in the event), automatically construct dictionaries for the existing information, cluster crime documents based on certain attributes and utilize visualisation techniques to assist in crime data analysis. The proposed information extraction approach is novel, and it relies on computational linguistic techniques to identify the abovementioned information, i.e. without using predefined dictionaries (e.g. lists of location names) and annotated corpus. The language used in crime reporting is studied to identify patterns of interest using a corpus-based approach. Frequency analysis, collocation analysis and concordance analysis are used to perform the syntactic analysis in order to discover the local grammar. Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering and visualisation tasks for crime documents based on crime type, location or nationality. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data is cleaned by removing noise. As a result, a huge reduction in the quantity of data fed into the SOM is obtained, consequently, saving memory, data loading time and the execution time needed to perform the clustering. Therefore, the computation of the SOM is accelerated. Finally, the quantization error is reduced, which leads to high quality clustering. The outcome of the clustering stage is also visualised and the system is able to provide statistical information in the form of graphs and tables about crimes committed within certain periods of time and within a particular area.Saudi Cultural Burea

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Epileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction

Epileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction