CALL FOR PARTICIPATION: EACL 2014 Tutorial on Recent Advances in Dependency Parsing

[peter ljunglöf]

CALL FOR PARTICIPATION

  EACL 2014 Tutorial: Recent Advances in Dependency Parsing
  Ryan McDonald, Joakim Nivre
  Gothenburg, Sweden, Sunday 27 April 2014
  http://eacl2014.org/tutorial-dependency-parsing


Syntactic parsing is a fundamental problem in natural language
processing which has been tackled using a wide variety of approaches.
In recent years, there has been a surge of interest in parsers that
make use of dependency structures, which offer a simple and
transparent encoding of predicate-argument structure and can be
derived accurately and efficiency using parsers trained on annotated
corpora. Thanks to their simplicity, transparency and efficiency,
dependency parsers are in widespread use for applications such as
information extraction, question answering, machine translation,
language modeling, semantic role labeling, and textual entailment.

This tutorial will focus on advances in dependency parsing that are
not covered in textbooks or previous tutorials, which means roughly
work from 2008 and onwards. However, in order to make the material
accessible to participants without a background in dependency parsing,
we will spend roughly the first quarter of the tutorial going over
basic concepts and techniques in the field, including the theoretical
foundations of dependency grammar and basic definitions of
representations, tasks, and evaluation metrics. After reviewing the
basic concepts, we will introduce the two dominant paradigms in early
work on data-driven dependency parsing -- global, exhaustive,
graph-based parsing and local, greedy, transition-based parsing -- and
review the contrastive error analysis presented in McDonald and Nivre
(2007), which highlighted the strengths and weaknesses of the two
models and set the challenge to improve both graph-based and
transition-based methods. This provides a basis for understanding many
of the later developments covered in the tutorial. The rest of the
tutorial will be divided into two main parts, covering advances in
graph-based parsing and related approaches, on the one hand, and
advances based on transition-based parsing, on the other. We will
finish off with a synthesizing conclusion and outlook for the future.

Research on graph-based dependency parsing in recent years has to a
large extent been driven by the wish to make efficient use of
higher-order models, thereby overcoming the limitations of strictly
local feature representations found in early models. As a consequence,
there has been developments towards specialized exact inference and
approximate inference methods, the latter especially for
non-projective parsing. In addition, there has been work on trying to
find exact dynamic programming solutions for restricted subsets of
non-projective structures, often referred to as mildly non-projective
dependency trees.

Recent work on transition-based dependency parsing has focused on two
lines of research, often in combination. The first line has been
concerned with improving the search techniques through the use of beam
search, dynamic programming, and easy-first inference, thereby
overcoming the limitations of greedy left-to-right search. The second
line has been to improve the learning methods by moving to global
structured learning and or imitation learning with exploration,
thereby countering the negative effects of local classifier learning.
In addition, there has been work on joint morphological and syntactic
analysis.