  The focus of this thesis is broadly on the alignment of lexicographical data,
particularly dictionaries. In order to tackle some of the challenges in this
field, two main tasks of word sense alignment and translation inference are
addressed. The first task aims to find an optimal alignment given the sense
definitions of a headword in two different monolingual dictionaries. This is a
challenging task, especially due to differences in sense granularity, coverage
and description in two resources. After describing the characteristics of
various lexical semantic resources, we introduce a benchmark containing 17
datasets of 15 languages where monolingual word senses and definitions are
manually annotated across different resources by experts. In the creation of
the benchmark, lexicographers' knowledge is incorporated through the
annotations where a semantic relation, namely exact, narrower, broader, related
or none, is selected for each sense pair. This benchmark can be used for
evaluation purposes of word-sense alignment systems. The performance of a few
alignment techniques based on textual and non-textual semantic similarity
detection and semantic relation induction is evaluated using the benchmark.
Finally, we extend this work to translation inference where translation pairs
are induced to generate bilingual lexicons in an unsupervised way using various
approaches based on graph analysis. This task is of particular interest for the
creation of lexicographical resources for less-resourced and under-represented
languages and also, assists in increasing coverage of the existing resources.
From a practical point of view, the techniques and methods that are developed
in this thesis are implemented within a tool that can facilitate the alignment
task.
