Data-driven grammar induction aims at producing wide-coverage grammars of
human languages. Initial efforts in this field produced relatively shallow
linguistic representations such as phrase-structure trees, which only
encode constituent structure. Recent work on inducing deep grammars from
treebanks addresses this shortcoming by also recovering non-local
dependencies and grammatical relations. My aim is to investigate the issues
arising when adapting an existing Lexical Functional Grammar (LFG)
induction method to a new language and treebank, and find solutions which
will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms
to learn morphological features, lemmatization classes and grammatical
functions from treebanks we can reduce the amount of manual specification
and improve robustness, accuracy and domain- and language-independence for
LFG parsing systems.
Function labels can often be relatively straightforwardly mapped to LFG
grammatical functions. Learning them reliably permits grammar induction to
depend less on language-specific LFG annotation rules. I therefore propose
ways to improve acquisition of function labels from treebanks and translate
those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of
syntactically relevant information comes from lexical entries. It is,
therefore, important to be able to perform morphological analysis in an
accurate and robust way for morphologically rich languages. I propose a
fully data-driven supervised method to simultaneously lemmatize and
morphologically analyze text and obtain competitive or improved results on
a range of typologically diverse languages.
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