A major scientific effort is dedicated to natural language understanding, which aims to be
able to comprehend text, reason about it, and act upon it in an intelligent way. While
specific use-cases or benchmarks can be solved with relatively simple systems, which either
ignore word order ("bag-of-words" models) or treat it as a simple linear structure (such as
the popular sequence-to-sequence framework allowing neural networks to learn tasks in an
end-to-end fashion), understanding human language in general requires a hierarchical
representation of meaning. Constructing this representation from text has been the goal of
an extensive line of work in semantic parsing. While many semantic representation schemes
have been proposed, they share many of their basic distinctions, such as between predicates
(relations, states and events) and arguments (participants). This thesis focuses on a
particular semantic representation scheme called Universal Conceptual Cognitive Annotation
(UCCA), whose main design principles are support for all major linguistic semantic
phenomena, cross-linguistic applicability, stability across translations, ease of annotation
(even by those who are not experts in linguistics), and a modular architecture supporting
multiple layers of semantic annotation. A fully automatic parser is presented, and evaluated
on multiple languages (English, French and German). The parser, titled "TUPA"
(transition-based UCCA parser), is able to learn very general graph structures: directed
acyclic graphs over token sequences with non-terminal nodes for complex units, where these
may cover discontinuous terminal yields. This general class of graphs covers the structures
annotated in UCCA, as well as other representation schemes. TUPA is implemented as a
transition-based parser, whose transition system supports these structural properties. Its
transition classifier is a neural network equipped with a bidirectional long short-term
memory (BiLSTM) module for calculating feature representations for the input. In an
extensive comparison to conversion-based methods, as well as other classifier
implementations, TUPA is shown to outperform all baselines in the task of UCCA parsing in
both in-domain and out-of-domain settings in three languages. The parser is subsequently
applied to two other semantic representation schemes, DM and AMR, and to syntactic
dependencies in the Universal Dependencies (UD) scheme. This demonstrates that the flexible
parser is usable not just for UCCA parsing. Furthermore, training TUPA in a multitask
setting on all of these schemes improves its UCCA parsing accuracy, by effectively learning
generalizations across the different representations: a shared model is thus able to apply
semantic distinctions in one task, which have been learned for another. Finally, in an
empirical comparison of the content of semantic and syntactic representations, we discover
several aspects of divergence, i.e., differences in the content captured by these schemes.
These have profound impact on the potential contribution of syntax to semantic parsing, and
on the usefulness of each of the approaches for semantic tasks in natural language
processing. I see semantic parsing as a means for computers to learn language. While
different representations focus on different distinctions and do so with formally different
structures, they share an overall goal, which is to support natural language processing
applications, such as classifying text into categories, tagging it for linguistic
properties, performing inference and reasoning, and generating new text according to some
constraints (e.g., machine translation). The combined datasets annotated in every
representation are an invaluable resource, which, used effectively, can greatly boost our
achievements in language understanding and processing.
