Methods for taking semantic graphs apart and putting them back together again

The thesis develops a competitive compositional semantic parser for Abstract Meaning Representation (AMR). This approach combines a neural model with mechanisms that echo ideas from compositional semantic construction in a new, simple dependency structure. The thesis first tackles the task of generating structured training data necessary for a compositional approach, by developing the linguistically motivated AM algebra. Encoding the terms over the AM algebra as dependency trees yields a simple semantic parsing model where neural tagging and dependency models predict interpretable, meaningful operations that construct the AMR.Diese Dissertation entwickelt einen kompositionellen semantischen Parser für den Graphformalismus Abstract Meaning Representation (AMR). Der Ansatz kombiniert ein neuronales Modell mit Mechanismen, die Ideen der klassischen kompositionellen semantischen Konstruktion widerspiegeln. Die Arbeit geht zunächst das Problem an, strukturierte latente Trainingsdaten zu erzeugen die für den kompositionellen Ansatz nötig sind. Für diesen Zweck wird die linguistisch motivierte AM Algebra entwickelt. Indem die Terme der AM Algebra als Dependenzbäume ausgedrückt werden, erhalten wir ein Modell für semantisches Parsen, in dem neuronale Tagging- und Dependenzmodelle interpretierbare, aussagekräftige Operationen vorhersagen die dann den AMR Graphen erzeugen. Damit erreicht das Modell starke Evaluationsergebnisse und deutliche Verbesserungen gegenüber einem weniger strukturierten Vergleichsmodell.DF

AMR Dependency Parsing with a Typed Semantic Algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see https://acl2018.org/programme/papers/

Fast semantic parsing with well-typedness guarantees

AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.Comment: Accepted at EMNLP 2020, camera-ready versio

Methods for taking semantic graphs apart and putting them back together again

Empirical thesis."PhD thesis developed at the Philosophische Fakultät, Saarland university and the Department of Computing at Macquarie University" -- title page.Bibliography: pages 223-230.1. Introduction -- 2. Background : semantic graphs, and building them piece by piece -- 3. Background : semantic parsing -- 4. S-graph decompisotion -- 5. The AM algebra -- 6. AM dependency parsing -- 7. Conclusion -- Bibliography -- Appendices.This thesis develops the AM dependency parser, a semantic parser for Abstract Meaning Representation (AMR, Banarescu et al. (2013)) that owes its strong performance to its effective combination of neural and compositional methods. Neural networks have proven to be enormously effective machine learning tools for natural language processing. Compositionality as a linguistic principle it has a strong tradition in semantic construction. However, both approaches have distinct challenges. Pure neural models are data hungry, since they have no prior knowledge of the inherent structure in language. Compositional approaches have robustness issues and suffer from the ambiguity of latent structural information in the training data.This thesis combines the strengths of both worlds to address these challenges. The AM dependency parser drops the restrictive syntactic constraints of classic compositional approaches, instead relying only on semantic types and meaningful semantic operations as structural guides. The ability of neural networks to encode contextual information allows the parser to make correct decisions in the absence of hard syntactic constraints.Consequently, the thesis focuses on terms for semantic representations, which are algebraic `building instructions'. The thesis frst examines the suitability of the HR algebra (a general tool for building graphs, Courcelle and Engelfriet (2012)) for this purpose. It then develops the linguistically motivated AM algebra, that proves much better suited for the purpose. Representing the terms over the AM algebra as dependency trees further simplifies the semantic construction. In particular, the move from the HR algebra to the AM algebra and then to AM dependency trees drastically removes the ambiguity of latent structural information required for training the model.In conclusion, the AM dependency trees yield a simple semantic parser, where neural tagging and dependency models predict interpretable, meaningful operations that construct the AMR.Mode of access: World wide web1 online resource (xviii, 244 pages) diagrams, graphs, table

Non-contrast-enhanced magnetic resonance imaging and computational fluid dynamics for carotid atherosclerosis

Empirical thesis.Bibliography: pages 223-230.Chapter 1. Introduction -- Chapter 2. Literature review -- Chapter 3. Imaging techniques for carotid atherosclerosis -- Chapter 4. New MRI sequence for the analysis of atherosclerotic plaques - Multicontrast Atherosclerosis Characterization (MATCH) -- Chapter 5. Computational fluid dynamics techniques and applications for cerebrovascular diseases -- Chapter 6. Associations between local haemodynamics and carotid intraplaque haemorrhage with different stenosis severities -- Chapter 7. Haemodynamic analysis of carotid artery after endarterectomy -- Chapter 8. Conclusions and future directions -- Ethics approval.Atherosclerotic plaques of the carotid artery are considered to be one of the major risk factors for stroke. To efficiently and precisely characterise the major plaque components, a new magnetic resonance imaging (MRI) sequence has therefore been promoted in this project. Besides plaque components, haemodynamics also plays a critical role in the progress and rupture of atherosclerotic plaques. Therefore, a new methodology that combines computational fluid dynamics (CFD) with non-contrast-enhanced magnetic resonance angiography (MRA) has been developed in this study to provide quantitative haemodynamic analysis for the diagnosis of carotid atherosclerosis and assessment of the outcomes of surgery.In the first part of this research, a newly-developed plaque sequence termed 'MATCH' is introduced. The MATCH sequence has the advantages of short acquisition time and ability to identify plaque components. The MATCH sequence has been utilised to characterise the major plaque components, which include intraplaque haemorrhage (IPH), a large lipid-rich necrotic core, loose matrix, and calcification. The performance of MATCH in differentiating plaque components was compared with that of conventional multi-contrast MRI and confirmed by histological evidence in this study. The results indicate that the MATCH was comparable if not superior to conventional protocol in identification and quantification of major carotid plaque components.In the second part, the relationship between haemodynamics and carotid plaques with IPH was investigated. We hypothesised that haemodynamics plays a pivotal role in the development of IPH. For this purpose, the maximum wall shear stress (mWSS) at the surface of plaques was compared for the groups with and without IPH under different severities of carotid artery stenosis. The results demonstrated that the higher mWSS was exhibited in carotids with IPH for cases with stenosis less than 70%, and the magnitude of mWSS was positively correlated with the volume of IPH. However, there was no significant relationship between mWSS and IPH when the stenosis exceeded 70%. Our results indicate that mWSS i a promising parameter to evaluate plaque vulnerability for carotids with stenosis of less than70%.In the last part of this thesis, CFD simulation was performed to analyse blood flow changes after carotid endarterectomy (CEA). The morphological characteristics and haemodynamic parameters were compared before and after CEA, as well as for healthy carotids as a control group. The major haemodynamic parameters were restored after surgery in short-term followup. This study indicates that CFD analysis can provide valuable information for the evaluation of physiological functions after CEA.Mode of access: World wide web1 online resource (181 pages) illustration

Graph parsing with s-graph grammars

A key problem in semantic parsing with graph-based semantic representations is graph parsing, i.e. computing all possible analyses of a given graph according to a grammar. This problem arises in training synchronous string-To-graph grammars, and when generating strings from them. We present two algorithms for graph parsing (bottom-up and top-down) with s-graph grammars. On the related problem of graph parsing with hyperedge replacement grammars, our implementations outperform the best previous system by several orders of magnitude.10 page(s