Learning with Joint Inference and Latent Linguistic Structure in Graphical Models

Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application. In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches. Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task

Aspect-based sentiment analysis for social recommender systems.

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To enhance recommendation performance, having an effective representation of products is essential. Social content such as product reviews contain experiential knowledge in the form of user opinions centred on product aspects. Making sense of these for recommender systems requires the capability to reason with text. However, Natural Language Processing (NLP) toolkits trained on formal text documents encounter challenges when analysing product reviews, due to their informal nature. This calls for novel methods and algorithms to capitalise on textual content in product reviews together with other knowledge resources. In this thesis, methods to utilise user purchase preference knowledge - inferred from the viewed and purchased product behaviour - are proposed to overcome the challenges encountered in analysing textual content. This thesis introduces three major methods to improve the performance of social recommender systems. First, an effective aspect extraction method that combines strengths of both dependency relations and frequent noun analysis is proposed. Thereafter, this thesis presents how extracted aspects can be used to structure opinionated content enabling sentiment knowledge to enrich product representations. Second, a novel method to integrate aspect-level sentiment analysis and implicit knowledge extracted from users' product purchase preferences analysis is presented. The role of sentiment distribution and threshold analysis on the proposed integration method is also explored. Third, this thesis explores the utility of feature selection techniques to rank and select relevant aspects for product representation. For this purpose, this thesis presents how established dimensionality reduction approaches from text classification can be employed to select a subset of aspects for recommendation purposes. Finally, a comprehensive evaluation of all the proposed methods in this thesis is presented using a computational measure of 'better' and Mean Average Precision (MAP) with seven real-world datasets

The role of sound offsets in auditory temporal processing and perception

Sound-offset responses are distinct to sound onsets in their underlying neural mechanisms, temporal processing pathways and roles in auditory perception following recent neurobiological studies. In this work, I investigate the role of sound offsets and the effect of reduced sensitivity to offsets on auditory perception in humans. The implications of a 'sound-offset deficit' for speech-in-noise perception are investigated, based on a mathematical model with biological significance and independent channels for onset and offset detection. Sound offsets are important in recognising, distinguishing and grouping sounds. They are also likely to play a role in perceiving consonants that lie in the troughs of amplitude fluctuations in speech. The offset influence on the discriminability of model outputs for 48 non-sense vowel-consonant-vowel (VCV) speech stimuli in varying levels of multi-talker babble noise (-12, -6, 0, 6, 12 dB SNR) was assessed, and led to predictions that correspond to known phonetic categories. This work therefore suggests that variability in the offset salience alone can explain the rank order of consonants most affected in noisy situations. A novel psychophysical test battery for offset sensitivity was devised and assessed, followed by a study to find an electrophysiological correlate. The findings suggest that individual differences in sound-offset sensitivity may be a factor contributing to inter-subject variation in speech-in-noise discrimination ability. The promising measures from these results can be used to test between-population differences in offset sensitivity, with more support for objective than psychophysical measures. In the electrophysiological study, offset responses in a duration discrimination paradigm were found to be modulated by attention compared to onset responses. Overall, this thesis shows for the first time that the onset-offset dichotomy in the auditory system, previously explored in physiological studies, is also evident in human studies for both simple and complex speech sounds

A Resource-Based Perspective

Geschäftsprozesstechnologien unterstützen dabei, operative Tätigkeiten effizienter und effektiver durchzuführen. In einem sich dynamisch verändernden Umfeld wird es für Organisationen essenziell, diese Technologien gezielt einzusetzen, um durch schnelle Anpassung weiterhin wettbewerbsfähig zu bleiben. Die derzeitige Forschung hat bisher keine Antwort darauf gefunden, wie Organisationen dies trotz ständig wechselnder Umfeldbedingungen und fortschreitender organisationaler Reife durch gezielte Ressourcenallokation erreichen können. Diese Dissertation adressiert diese Forschungslücke, indem untersucht wird, wie organisationale Fähigkeiten mithilfe von Geschäftsprozesstechnologien innerhalb dynamischer Umfelder ausgebildet und erneuert werden können.Business process technologies help to improve the efficiency and effectiveness of day-to-day operations. Organizations face the challenge of leveraging these technologies to quickly adapt business processes accordingly to cope with different levels of environmental turbulence. From prior research, we know how organizations apply business process technologies and how they affect performance. We do not fully understand how organizations orchestrate related resources based on changing environmental conditions and evolving organizational maturity. This dissertation addresses this research problem and presents research on how to develop and renew organizational capabilities with business process technologies through turbulent environments

Discovering Lexical Generalisations. A Supervised Machine Learning Approach to Inheritance Hierarchy Construction

Institute for Communicating and Collaborative SystemsGrammar development over the last decades has seen a shift away from large inventories of grammar rules to richer lexical structures. Many modern grammar theories are highly lexicalised. But simply listing lexical entries typically results in an undesirable amount of redundancy. Lexical inheritance hierarchies, on the other hand, make it possible to capture linguistic generalisations and thereby reduce redundancy. Inheritance hierarchies are usually constructed by hand but this is time-consuming and often impractical if a lexicon is very large. Constructing hierarchies automatically or semiautomatically facilitates a more systematic analysis of the lexical data. In addition, lexical data is often extracted automatically from corpora and this is likely to increase over the coming years. Therefore it makes sense to go a step further and automate the hierarchical organisation of lexical data too. Previous approaches to automatic lexical inheritance hierarchy construction tended to focus on minimality criteria, aiming for hierarchies that minimised one or more criteria such as the number of path-value pairs, the number of nodes or the number of inheritance links (Petersen 2001, Barg 1996a, and in a slightly different context: Light 1994). Aiming for minimality is motivated by the fact that the conciseness of inheritance hierarchies is a main reason for their use. However, I will argue that there are several problems with minimality-based approaches. First, minimality is not well defined in the context of lexical inheritance hierarchies as there is a tension between different minimality criteria. Second, minimality-based approaches tend to underestimate the importance of linguistic plausibility. While such approaches start with a definition of minimal redundancy and then try to prove that this leads to plausible hierarchies, the approach suggested here takes the opposite direction. It starts with a manually built hierarchy to which a supervised machine learning algorithm is applied with the aim of finding a set of formal criteria that can guide the construction of plausible hierarchies. Taking this direction means that it is more likely that the selected criteria do in fact lead to plausible hierarchies. Using a machine learning technique also has the advantage that the set of criteria can be much larger than in hand-crafted definitions. Consequently, one can define conciseness in very broad terms, taking into account interdependencies in the data as well as simple minimality criteria. This leads to a more fine-grained model of hierarchy quality. In practice, the method proposed here consists of two components: Galois lattices are used to define the search space as the set of all generalisations over the input lexicon. Maximum entropy models which have been trained on a manually built hierarchy are then applied to the lattice of the input lexicon to distinguish between plausible and implausible generalisations based on the formal criteria that were found in the training step. An inheritance hierarchy is then derived by pruning implausible generalisations. The hierarchy is automatically evaluated by matching it to a manually built hierarchy for the input lexicon. Automatically constructing lexical hierarchies is a hard task, partly because what is considered the best hierarchy for a lexicon is to some extent subjective. Supervised learning methods also suffer from a lack of suitable training data. Hence, a semi-automatic architecture may be best suited for the task. Therefore, the performance of the system has been tested using a semi-automatic as well as an automatic architecture and it has also been compared to the performance achieved by the pruning algorithm suggested by Petersen (2001). The findings show that the method proposed here is well suited for semi-automatic hierarchy construction

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Geopolitics and internal power structures: The state, police and public order in Austria and Ireland in the late 18th century.

In this thesis I contribute to the sociological discussion on the impact of geopolitical constellations on the class and cleavage structure of societies. The main concern is to analyse how the capacity of collective social actors to pursue their interests against other antagonistic collective actors can be impeded, or increased, by relations of violence between the state in which they operate and foreign states. This problem is developed in a first step by a review of the sociological literature on the formation of the modern state in Western Europe. A close scrutiny of the explanatory strengths and weaknesses of both the 'society-centred' and the 'state-centred' approaches leads to the conclusion that an adequate analysis of political structural change in Western Europe has to emphasize the dynamic interplay of political, cultural, economic and geopolitical structures of social action. In the two case studies on Austria and Ireland in the 18th century, I discuss the interaction between class, political, regional/colonial, and ideological power groupings and economic, ideological, political and geopolitical interests. I show how the conflict structures of both Austria and Ireland gained momentum due to geopolitical constellations. I analyse how the attempts of the Austrian and the Irish state to establish police forces under their own exclusive control and to maintain public order were related to geopolitics. In order to explain the power capacity of these two states I analyse the effect of geopolitics on the distribution of power within the respective society