219 research outputs found
Universal Dependencies Parsing for Colloquial Singaporean English
Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.Comment: Accepted by ACL 201
Harmonization and Merging of two Italian Dependency Treebanks
The paper describes the methodology which is currently being defined for the construction of a "Merged Italian Dependency Treebank'' (MIDT) starting from already existing resources. In particular, it reports the results of a case study carried out on two available dependency treebanks, i.e. TUT and ISST--TANL. The issues raised during the comparison of the annotation schemes underlying the two treebanks are discussed and investigated with a particular emphasis on the definition of a set of linguistic categories to be used as a "bridge'' between the specific schemes. As an encoding format, the CoNLL de facto standard is used
Apportioning Development Effort in a Probabilistic LR Parsing System through Evaluation
We describe an implemented system for robust domain-independent syntactic
parsing of English, using a unification-based grammar of part-of-speech and
punctuation labels coupled with a probabilistic LR parser. We present
evaluations of the system's performance along several different dimensions;
these enable us to assess the contribution that each individual part is making
to the success of the system as a whole, and thus prioritise the effort to be
devoted to its further enhancement. Currently, the system is able to parse
around 80% of sentences in a substantial corpus of general text containing a
number of distinct genres. On a random sample of 250 such sentences the system
has a mean crossing bracket rate of 0.71 and recall and precision of 83% and
84% respectively when evaluated against manually-disambiguated analyses.Comment: 10 pages, 1 Postscript figure. To Appear in Proceedings of the
Conference on Empirical Methods in Natural Language Processing, University of
Pennsylvania, May 199
Masking Treebanks for the Free Distribution of Linguistic Resources and Other Applications
Proceedings of the Sixth International Workshop on Treebanks and
Linguistic Theories.
Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler.
NEALT Proceedings Series, Vol. 1 (2007), 127-138.
© 2007 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/4476
What to do about non-standard (or non-canonical) language in NLP
Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.Comment: KONVENS 201
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