Trend Analysis in AI Research over time Using NLP Techniques

The dramatic rise in the number of publications in machine learning related studies poses a challenge for companies and new researchers when they want to focus their resources effectively. This thesis aims to provide an automatic pipeline to extract the most relevant trends in the machine learning field. I applied unsupervised topic modeling methods to discover research trends from full NIPS conference papers from 1987 to 2018. By comparing the Latent Dirichlet Allocation (LDA) topic model with a model utilizing semantic word vectors (sHDP), it was shown that the LDA performed better in both quality and coherence. Using the LDA, 50 topics were extracted and interpreted to match the key concepts in the conference publications. The results revealed three distinct eras in the NIPS history as well as the steady shift away from the neural information processing roots towards deep learning

Text mining and natural language processing for the early stages of space mission design

Final thesis submitted December 2021 - degree awarded in 2022A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes.A considerable amount of data related to space mission design has been accumulated since artificial satellites started to venture into space in the 1950s. This data has today become an overwhelming volume of information, triggering a significant knowledge reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants, text mining and Natural Language Processing techniques have become pervasive to our daily life. The work presented in this thesis is one of the first attempts to bridge the gap between the worlds of space systems engineering and text mining. Several novel models are thus developed and implemented here, targeting the structuring of accumulated data through an ontology, but also tasks commonly performed by systems engineers such as requirement management and heritage analysis. A first collection of documents related to space systems is gathered for the training of these methods. Eventually, this work aims to pave the way towards the development of a Design Engineering Assistant (DEA) for the early stages of space mission design. It is also hoped that this work will actively contribute to the integration of text mining and Natural Language Processing methods in the field of space mission design, enhancing current design processes

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

Computational approaches to semantic change (Volume 6)

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans

Understanding the topics and opinions from social media content

Social media has become one indispensable part of people’s daily life, as it records and reflects people’s opinions and events of interest, as well as influences people’s perceptions. As the most commonly employed and easily accessed data format on social media, a great deal of the social media textual content is not only factual and objective, but also rich in opinionated information. Thus, besides the topics Internet users are talking about in social media textual content, it is also of great importance to understand the opinions they are expressing. In this thesis, I present my broadly applicable text mining approaches, in order to understand the topics and opinions of user-generated texts on social media, to provide insights about the thoughts of Internet users on entities, events, etc. Specifically, I develop approaches to understand the semantic differences between language-specific editions of Wikipedia, when discussing certain entities from the related topical aspects perspective and the aggregated sentiment bias perspective. Moreover, I employ effective features to detect the reputation-influential sentences for person and company entities in Wikipedia articles, which lead to the detected sentiment bias. Furthermore, I propose neural network models with different levels of attention mechanism, to detect the stances of tweets towards any given target. I also introduce an online timeline generation approach, to detect and summarise the relevant sub-topics in the tweet stream, in order to provide Internet users with some insights about the evolution of major events they are interested in

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives