Identification of Online Users' Social Status via Mining User-Generated Data

With the burst of available online user-generated data, identifying online users’ social status via mining user-generated data can play a significant role in many commercial applications, research and policy-making in many domains. Social status refers to the position of a person in relation to others within a society, which is an abstract concept. The actual definition of social status is specific in terms of specific measure indicator. For example, opinion leadership measures individual social status in terms of influence and expertise in an online society, while socioeconomic status characterizes personal real-life social status based on social and economic factors. Compared with traditional survey method which is time-consuming, expensive and sometimes difficult, some efforts have been made to identify specific social status of users based on specific user-generated data using classic machine learning methods. However, in fact, regarding specific social status identification based on specific user-generated data, the specific case has several specific challenges. However, classic machine learning methods in existing works fail to address these challenges, which lead to low identification accuracy. Given the importance of improving identification accuracy, this thesis studies three specific cases on identification of online and offline social status. For each work, this thesis proposes novel effective identification method to address the specific challenges for improving accuracy. The first work aims at identifying users’ online social status in terms of topic-sensitive influence and knowledge authority in social community question answering sites, namely identifying topical opinion leaders who are both influential and expert. Social community question answering (SCQA) site, an innovative community question answering platform, not only offers traditional question answering (QA) services but also integrates an online social network where users can follow each other. Identifying topical opinion leaders in SCQA has become an important research area due to the significant role of topical opinion leaders. However, most previous related work either focus on using knowledge expertise to find experts for improving the quality of answers, or aim at measuring user influence to identify influential ones. In order to identify the true topical opinion leaders, we propose a topical opinion leader identification framework called QALeaderRank which takes account of both topic-sensitive influence and topical knowledge expertise. In the proposed framework, to measure the topic-sensitive influence of each user, we design a novel influence measure algorithm that exploits both the social and QA features of SCQA, taking into account social network structure, topical similarity and knowledge authority. In addition, we propose three topic-relevant metrics to infer the topical expertise of each user. The extensive experiments along with an online user study show that the proposed QALeaderRank achieves significant improvement compared with the state-of-the-art methods. Furthermore, we analyze the topic interest change behaviors of users over time and examine the predictability of user topic interest through experiments. The second work focuses on predicting individual socioeconomic status from mobile phone data. Socioeconomic Status (SES) is an important social and economic aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised Hypergraph based Factor Graph Model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on individual SES prediction with using a set of anonymized real mobile phone data. The third work is to predict social media users’ socioeconomic status based on their social media content, which is useful for related organizations and companies in a range of applications, such as economic and social policy-making. Previous work leverage manually defined textual features and platform-based user level attributes from social media content and feed them into a machine learning based classifier for SES prediction. However, they ignore some important information of social media content, containing the order and the hierarchical structure of social media text as well as the relationships among user level attributes. To this end, we propose a novel coupled social media content representation model for individual SES prediction, which not only utilizes a hierarchical neural network to incorporate the order and the hierarchical structure of social media text but also employs a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. The experimental results show that the proposed model significantly outperforms other stat-of-the-art models on a real dataset, which validate the efficiency and robustness of the proposed model

Addressing the data bottleneck in implicit discourse relation classification

When humans comprehend language, their interpretation consists of more than just the sum of the content of the sentences. Additional logic and semantic links (known as coherence relations or discourse relations) are inferred between sentences/clauses in the text. The identification of discourse relations is beneficial for various NLP applications such as question-answering, summarization, machine translation, information extraction, etc. Discourse relations are categorized into implicit and explicit discourse relations depending on whether there is an explicit discourse marker between the arguments. In this thesis, we mainly focus on the implicit discourse relation classification, given that with the explicit markers acting as informative cues, the explicit relations are relatively easier to identify for machines. The recent neural network-based approaches in particular suffer from insufficient training (and test) data. As shown in Chapter 3 of this thesis, we start out by showing to what extent the limited data size is a problem in implicit discourse relation classification and propose data augmentation methods with the help of cross-lingual data. And then we propose several approaches for better exploiting and encoding various types of existing data in the discourse relation classification task. Most of the existing machine learning methods train on sections 2-21 of the PDTB and test on section 23, which only includes a total of less than 800 implicit discourse relation instances. With the help of cross validation, we argue that the standard test section of the PDTB is too small to draw conclusions upon. With more test samples in the cross validation, we would come to very different conclusions about whether a feature is generally useful. Second, we propose a simple approach to automatically extract samples of implicit discourse relations from multilingual parallel corpus via back-translation. After back-translating from target languages, it is easy for the discourse parser to identify those examples that are originally implicit but explicit in the back-translations. Having those additional data in the training set, the experiments show significant improvements on different settings. Finally, having better encoding ability is also of crucial importance in terms of improving classification performance. We propose different methods including a sequence-to-sequence neural network and a memory component to help have a better representation of the arguments. We also show that having the correct next sentence is beneficial for the task within and across domains, with the help of the BERT (Devlin et al., 2019) model. When it comes to a new domain, it is beneficial to integrate external domain-specific knowledge. In Chapter 8, we show that with the entity-enhancement, the performance on BioDRB is improved significantly, comparing with other BERT-based methods. In sum, the studies reported in this dissertation contribute to addressing the data bottleneck problem in implicit discourse relation classification and propose corresponding approaches that achieve 54.82% and 69.57% on PDTB and BioDRB respectively.Wenn Menschen Sprache verstehen, besteht ihre Interpretation aus mehr als nur der Summe des Inhalts der Sätze. Zwischen Sätzen im Text werden zusätzliche logische und semantische Verknüpfungen (sogenannte Kohärenzrelationen oder Diskursrelationen) hergeleitet. Die Identifizierung von Diskursrelationen ist für verschiedene NLP-Anwendungen wie Frage- Antwort, Zusammenfassung, maschinelle Übersetzung, Informationsextraktion usw. von Vorteil. Diskursrelationen werden in implizite und explizite Diskursrelationen unterteilt, je nachdem, ob es eine explizite Diskursrelationen zwischen den Argumenten gibt. In dieser Arbeit konzentrieren wir uns hauptsächlich auf die Klassifizierung der impliziten Diskursrelationen, da die expliziten Marker als hilfreiche Hinweise dienen und die expliziten Beziehungen für Maschinen relativ leicht zu identifizieren sind. Es wurden verschiedene Ansätze vorgeschlagen, die bei der impliziten Diskursrelationsklassifikation beeindruckende Ergebnisse erzielt haben. Die meisten von ihnen leiden jedoch darunter, dass die Daten für auf neuronalen Netzen basierende Methoden unzureichend sind. In dieser Arbeit gehen wir zunächst auf das Problem begrenzter Daten bei dieser Aufgabe ein und schlagen dann Methoden zur Datenanreicherung mit Hilfe von sprachübergreifenden Daten vor. Zuletzt schlagen wir mehrere Methoden vor, um die Argumente aus verschiedenen Aspekten besser kodieren zu können. Die meisten der existierenden Methoden des maschinellen Lernens werden auf den Abschnitten 2-21 der PDTB trainiert und auf dem Abschnitt 23 getestet, der insgesamt nur weniger als 800 implizite Diskursrelationsinstanzen enthält. Mit Hilfe der Kreuzvalidierung argumentieren wir, dass der Standardtestausschnitt der PDTB zu klein ist um daraus Schlussfolgerungen zu ziehen. Mit mehr Teststichproben in der Kreuzvalidierung würden wir zu anderen Schlussfolgerungen darüber kommen, ob ein Merkmal für diese Aufgabe generell vorteilhaft ist oder nicht, insbesondere wenn wir einen relativ großen Labelsatz verwenden. Wenn wir nur unseren kleinen Standardtestsatz herausstellen, laufen wir Gefahr, falsche Schlüsse darüber zu ziehen, welche Merkmale hilfreich sind. Zweitens schlagen wir einen einfachen Ansatz zur automatischen Extraktion von Samples impliziter Diskursrelationen aus mehrsprachigen Parallelkorpora durch Rückübersetzung vor. Er ist durch den Explikationsprozess motiviert, wenn Menschen einen Text übersetzen. Nach der Rückübersetzung aus den Zielsprachen ist es für den Diskursparser leicht, diejenigen Beispiele zu identifizieren, die ursprünglich implizit, in den Rückübersetzungen aber explizit enthalten sind. Da diese zusätzlichen Daten im Trainingsset enthalten sind, zeigen die Experimente signifikante Verbesserungen in verschiedenen Situationen. Wir verwenden zunächst nur französisch-englische Paare und haben keine Kontrolle über die Qualität und konzentrieren uns meist auf die satzinternen Relationen. Um diese Fragen in Angriff zu nehmen, erweitern wir die Idee später mit mehr Vorverarbeitungsschritten und mehr Sprachpaaren. Mit den Mehrheitsentscheidungen aus verschiedenen Sprachpaaren sind die gemappten impliziten Labels zuverlässiger. Schließlich ist auch eine bessere Kodierfähigkeit von entscheidender Bedeutung für die Verbesserung der Klassifizierungsleistung. Wir schlagen ein neues Modell vor, das aus einem Klassifikator und einem Sequenz-zu-Sequenz-Modell besteht. Neben der korrekten Vorhersage des Labels werden sie auch darauf trainiert, eine Repräsentation der Diskursrelationsargumente zu erzeugen, indem sie versuchen, die Argumente einschließlich eines geeigneten impliziten Konnektivs vorherzusagen. Die neuartige sekundäre Aufgabe zwingt die interne Repräsentation dazu, die Semantik der Relationsargumente vollständiger zu kodieren und eine feinkörnigere Klassifikation vorzunehmen. Um das allgemeine Wissen in Kontexten weiter zu erfassen, setzen wir auch ein Gedächtnisnetzwerk ein, um eine explizite Kontextrepräsentation von Trainingsbeispielen für Kontexte zu erhalten. Für jede Testinstanz erzeugen wir durch gewichtetes Lesen des Gedächtnisses einen Wissensvektor. Wir evaluieren das vorgeschlagene Modell unter verschiedenen Bedingungen und die Ergebnisse zeigen, dass das Modell mit dem Speichernetzwerk die Vorhersage von Diskursrelationen erleichtern kann, indem es Beispiele auswählt, die eine ähnliche semantische Repräsentation und Diskursrelationen aufweisen. Auch wenn ein besseres Verständnis, eine Kodierung und semantische Interpretation für die Aufgabe der impliziten Diskursrelationsklassifikation unerlässlich und nützlich sind, so leistet sie doch nur einen Teil der Arbeit. Ein guter impliziter Diskursrelationsklassifikator sollte sich auch der bevorstehenden Ereignisse, Ursachen, Folgen usw. bewusst sein, um die Diskurserwartung in die Satzdarstellungen zu kodieren. Mit Hilfe des kürzlich vorgeschlagenen BERT-Modells versuchen wir herauszufinden, ob es für die Aufgabe vorteilhaft ist, den richtigen nächsten Satz zu haben oder nicht. Die experimentellen Ergebnisse zeigen, dass das Entfernen der Aufgabe zur Vorhersage des nächsten Satzes die Leistung sowohl innerhalb der Domäne als auch domänenübergreifend stark beeinträchtigt. Die begrenzte Fähigkeit von BioBERT, domänenspezifisches Wissen, d.h. Entitätsinformationen, Entitätsbeziehungen etc. zu erlernen, motiviert uns, externes Wissen in die vortrainierten Sprachmodelle zu integrieren. Wir schlagen eine unüberwachte Methode vor, bei der Information-Retrieval-System und Wissensgraphen-Techniken verwendet werden, mit der Annahme, dass, wenn zwei Instanzen ähnliche Entitäten in beiden relationalen Argumenten teilen, die Wahrscheinlichkeit groß ist, dass sie die gleiche oder eine ähnliche Diskursrelation haben. Der Ansatz erzielt vergleichbare Ergebnisse auf BioDRB, verglichen mit Baselinemodellen. Anschließend verwenden wir die extrahierten relevanten Entitäten zur Verbesserung des vortrainierten Modells K-BERT, um die Bedeutung der Argumente besser zu kodieren und das ursprüngliche BERT und BioBERT mit einer Genauigkeit von 6,5% bzw. 2% zu übertreffen. Zusammenfassend trägt diese Dissertation dazu bei, das Problem des Datenengpasses bei der impliziten Diskursrelationsklassifikation anzugehen, und schlägt entsprechende Ansätze in verschiedenen Aspekten vor, u.a. die Darstellung des begrenzten Datenproblems und der Risiken bei der Schlussfolgerung daraus; die Erfassung automatisch annotierter Daten durch den Explikationsprozess während der manuellen Übersetzung zwischen Englisch und anderen Sprachen; eine bessere Repräsentation von Diskursrelationsargumenten; Entity-Enhancement mit einer unüberwachten Methode und einem vortrainierten Sprachmodell

Modelling input texts: from Tree Kernels to Deep Learning

One of the core questions when designing modern Natural Language Processing (NLP) systems is how to model input textual data such that the learning algorithm is provided with enough information to estimate accurate decision functions. The mainstream approach is to represent input objects as feature vectors where each value encodes some of their aspects, e.g., syntax, semantics, etc. Feature-based methods have demonstrated state-of-the-art results on various NLP tasks. However, designing good features is a highly empirical-driven process, it greatly depends on a task requiring a significant amount of domain expertise. Moreover, extracting features for complex NLP tasks often requires expensive pre-processing steps running a large number of linguistic tools while relying on external knowledge sources that are often not available or hard to get. Hence, this process is not cheap and often constitutes one of the major challenges when attempting a new task or adapting to a different language or domain. The problem of modelling input objects is even more acute in cases when the input examples are not just single objects but pairs of objects, such as in various learning to rank problems in Information Retrieval and Natural Language processing. An alternative to feature-based methods is using kernels which are essentially non-linear functions mapping input examples into some high dimensional space thus allowing for learning decision functions with higher discriminative power. Kernels implicitly generate a very large number of features computing similarity between input examples in that implicit space. A well-designed kernel function can greatly reduce the effort to design a large set of manually designed features often leading to superior results. However, in the recent years, the use of kernel methods in NLP has been greatly under-estimated primarily due to the following reasons: (i) learning with kernels is slow as it requires to carry out optimization in the dual space leading to quadratic complexity; (ii) applying kernels to the input objects encoded with vanilla structures, e.g., generated by syntactic parsers, often yields minor improvements over carefully designed feature-based methods. In this thesis, we adopt the kernel learning approach for solving complex NLP tasks and primarily focus on solutions to the aforementioned problems posed by the use of kernels. In particular, we design novel learning algorithms for training Support Vector Machines with structural kernels, e.g., tree kernels, considerably speeding up the training over the conventional SVM training methods. We show that using the training algorithms developed in this thesis allows for training tree kernel models on large-scale datasets containing millions of instances, which was not possible before. Next, we focus on the problem of designing input structures that are fed to tree kernel functions to automatically generate a large set of tree-fragment features. We demonstrate that previously used plain structures generated by syntactic parsers, e.g., syntactic or dependency trees, are often a poor choice thus compromising the expressivity offered by a tree kernel learning framework. We propose several effective design patterns of the input tree structures for various NLP tasks ranging from sentiment analysis to answer passage reranking. The central idea is to inject additional semantic information relevant for the task directly into the tree nodes and let the expressive kernels generate rich feature spaces. For the opinion mining tasks, the additional semantic information injected into tree nodes can be word polarity labels, while for more complex tasks of modelling text pairs the relational information about overlapping words in a pair appears to significantly improve the accuracy of the resulting models. Finally, we observe that both feature-based and kernel methods typically treat words as atomic units where matching different yet semantically similar words is problematic. Conversely, the idea of distributional approaches to model words as vectors is much more effective in establishing a semantic match between words and phrases. While tree kernel functions do allow for a more flexible matching between phrases and sentences through matching their syntactic contexts, their representation can not be tuned on the training set as it is possible with distributional approaches. Recently, deep learning approaches have been applied to generalize the distributional word matching problem to matching sentences taking it one step further by learning the optimal sentence representations for a given task. Deep neural networks have already claimed state-of-the-art performance in many computer vision, speech recognition, and natural language tasks. Following this trend, this thesis also explores the virtue of deep learning architectures for modelling input texts and text pairs where we build on some of the ideas to model input objects proposed within the tree kernel learning framework. In particular, we explore the idea of relational linking (proposed in the preceding chapters to encode text pairs using linguistic tree structures) to design a state-of-the-art deep learning architecture for modelling text pairs. We compare the proposed deep learning models that require even less manual intervention in the feature design process then previously described tree kernel methods that already offer a very good trade-off between the feature-engineering effort and the expressivity of the resulting representation. Our deep learning models demonstrate the state-of-the-art performance on a recent benchmark for Twitter Sentiment Analysis, Answer Sentence Selection and Microblog retrieval

Distributed representations for multilingual language processing

Distributed representations are a central element in natural language processing. Units of text such as words, ngrams, or characters are mapped to real-valued vectors so that they can be processed by computational models. Representations trained on large amounts of text, called static word embeddings, have been found to work well across a variety of tasks such as sentiment analysis or named entity recognition. More recently, pretrained language models are used as contextualized representations that have been found to yield even better task performances. Multilingual representations that are invariant with respect to languages are useful for multiple reasons. Models using those representations would only require training data in one language and still generalize across multiple languages. This is especially useful for languages that exhibit data sparsity. Further, machine translation models can benefit from source and target representations in the same space. Last, knowledge extraction models could not only access English data, but data in any natural language and thus exploit a richer source of knowledge. Given that several thousand languages exist in the world, the need for multilingual language processing seems evident. However, it is not immediately clear, which properties multilingual embeddings should exhibit, how current multilingual representations work and how they could be improved. This thesis investigates some of these questions. In the first publication, we explore the boundaries of multilingual representation learning by creating an embedding space across more than one thousand languages. We analyze existing methods and propose concept based embedding learning methods. The second paper investigates differences between creating representations for one thousand languages with little data versus considering few languages with abundant data. In the third publication, we refine a method to obtain interpretable subspaces of embeddings. This method can be used to investigate the workings of multilingual representations. The fourth publication finds that multilingual pretrained language models exhibit a high degree of multilinguality in the sense that high quality word alignments can be easily extracted. The fifth paper investigates reasons why multilingual pretrained language models are multilingual despite lacking any kind of crosslingual supervision during training. Based on our findings we propose a training scheme that leads to improved multilinguality. Last, the sixth paper investigates the use of multilingual pretrained language models as multilingual knowledge bases

Spatio-Temporal Multimedia Big Data Analytics Using Deep Neural Networks

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era, where new opportunities and challenges appear with the high diversity multimedia data together with the huge amount of social data. Nowadays, multimedia data consisting of audio, text, image, and video has grown tremendously. With such an increase in the amount of multimedia data, the main question raised is how one can analyze this high volume and variety of data in an efficient and effective way. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, there is insufficient research that provides a comprehensive framework for multimedia big data analytics and management. To address the major challenges in this area, a new framework is proposed based on deep neural networks for multimedia semantic concept detection with a focus on spatio-temporal information analysis and rare event detection. The proposed framework is able to discover the pattern and knowledge of multimedia data using both static deep data representation and temporal semantics. Specifically, it is designed to handle data with skewed distributions. The proposed framework includes the following components: (1) a synthetic data generation component based on simulation and adversarial networks for data augmentation and deep learning training, (2) an automatic sampling model to overcome the imbalanced data issue in multimedia data, (3) a deep representation learning model leveraging novel deep learning techniques to generate the most discriminative static features from multimedia data, (4) an automatic hyper-parameter learning component for faster training and convergence of the learning models, (5) a spatio-temporal deep learning model to analyze dynamic features from multimedia data, and finally (6) a multimodal deep learning fusion model to integrate different data modalities. The whole framework has been evaluated using various large-scale multimedia datasets that include the newly collected disaster-events video dataset and other public datasets

Exploiting semantic similarity models to automate transfer credit assessment in academic mobility

Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes (LOs) of the courses, and based on their similarity deciding on offering transfer credits to the incoming students. This manual im- plementation of the task is not only labor-intensive but also influenced by undue bias and administrative complexity. This research work focuses on identifying an algorithm that ex- ploits the advancements in the field of Natural Language Processing (NLP) to effectively automate this process. A survey tracing the evolution of semantic similarity helps under- stand the various methods available to calculate the semantic similarity between text data. The basic units of comparison namely, learning outcomes are made up of two components namely the descriptor part which provides the contents covered, and the action word which provides the competency achieved. Bloom’s taxonomy provides six different levels of com- petency to which the action words fall into. Given the unique structure, domain specificity, and complexity of learning outcomes, a need for designing a tailor-made algorithm arises. The proposed algorithm uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of learning outcomes and a transformer-based semantic similarity model to assess the semantic similarity of the learning outcomes. The cumulative similarity between the learning outcomes is further aggregated to form course to course similarity. Due to the lack of quality benchmark datasets, a new benchmark dataset is built by conducting a survey among domain experts with knowledge in both academia and computer science. The dataset contains 7 course-to-course similarity values annotated by 5 domain experts. Understanding the inherent need for flexibility in the decision-making process the aggregation part of the algorithm offers tunable parame- ters to accommodate different scenarios. Being one of the early research works in the field of automating articulation, this thesis establishes the imminent challenges that need to be addressed in the field namely, the significant decrease in performance by state-of-the-art se- mantic similarity models with an increase in complexity of sentences, lack of large datasets to train/fine-tune existing models, lack of quality in available learning outcomes, and reluc- tance to share learning outcomes publicly. While providing an efficient algorithm to assess the similarity between courses with existing resources, this research work steers future re- search attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Representation learning in complex data via pattern discovery

This study proposes effective methods to learn meaningful representations for complex data such as sequences and graphs. It combines two important techniques in data mining and machine learning: pattern discovery and representation learning. The proposed methods can be applied to different real-world problems including healthcare analysis, business marketing, and bioinformatic

WELLNESS PROFILING ON SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH