10 research outputs found
Introduction to the CoNLL-2001 Shared Task: Clause Identification
We describe the CoNLL-2001 shared task: dividing text into clauses. We give
background information on the data sets, present a general overview of the
systems that have taken part in the shared task and briefly discuss their
performance
Introduction to the CoNLL-2000 Shared Task: Chunking
We describe the CoNLL-2000 shared task: dividing text into syntactically
related non-overlapping groups of words, so-called text chunking. We give
background information on the data sets, present a general overview of the
systems that have taken part in the shared task and briefly discuss their
performance.Comment: 6 page
Memory-Based Shallow Parsing
We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement
Automatic identification and translation of multiword expressions
A thesis submitted in partial fulfilment of the requirements of the
University of Wolverhampton for the degree of Doctor of Philosophy.Multiword Expressions (MWEs) belong to a class of phraseological phenomena
that is ubiquitous in the study of language. They are heterogeneous
lexical items consisting of more than one word and feature lexical, syntactic,
semantic and pragmatic idiosyncrasies. Scholarly research on MWEs benefits
both natural language processing (NLP) applications and end users.
This thesis involves designing new methodologies to identify and translate
MWEs. In order to deal with MWE identification, we first develop datasets
of annotated verb-noun MWEs in context. We then propose a method which
employs word embeddings to disambiguate between literal and idiomatic usages
of the verb-noun expressions. Existence of expression types with various
idiomatic and literal distributions leads us to re-examine their modelling and
evaluation. We propose a type-aware train and test splitting approach to
prevent models from overfitting and avoid misleading evaluation results.
Identification of MWEs in context can be modelled with sequence tagging
methodologies. To this end, we devise a new neural network architecture,
which is a combination of convolutional neural networks and long-short
term memories with an optional conditional random field layer on top. We
conduct extensive evaluations on several languages demonstrating a better
performance compared to the state-of-the-art systems. Experiments show that the generalisation power of the model in predicting unseen MWEs is significantly better than previous systems.
In order to find translations for verb-noun MWEs, we propose a bilingual
distributional similarity approach derived from a word embedding model that
supports arbitrary contexts. The technique is devised to extract translation
equivalents from comparable corpora which are an alternative resource to
costly parallel corpora. We finally conduct a series of experiments to investigate
the effects of size and quality of comparable corpora on automatic
extraction of translation equivalents