265 research outputs found
An Empirical Comparison of Parsing Methods for Stanford Dependencies
Stanford typed dependencies are a widely desired representation of natural
language sentences, but parsing is one of the major computational bottlenecks
in text analysis systems. In light of the evolving definition of the Stanford
dependencies and developments in statistical dependency parsing algorithms,
this paper revisits the question of Cer et al. (2010): what is the tradeoff
between accuracy and speed in obtaining Stanford dependencies in particular? We
also explore the effects of input representations on this tradeoff:
part-of-speech tags, the novel use of an alternative dependency representation
as input, and distributional representaions of words. We find that direct
dependency parsing is a more viable solution than it was found to be in the
past. An accompanying software release can be found at:
http://www.ark.cs.cmu.edu/TBSDComment: 13 pages, 2 figure
From news to comment: Resources and benchmarks for parsing the language of web 2.0
We investigate the problem of parsing the noisy language of social media. We evaluate four all-Street-Journal-trained statistical parsers (Berkeley, Brown, Malt and MST) on a new dataset containing 1,000 phrase structure trees for sentences from microblogs (tweets) and discussion forum posts. We compare the four parsers on their ability to produce Stanford dependencies for these Web 2.0 sentences. We find that the parsers have a particular problem with tweets and that a substantial part of this problem is related to POS tagging accuracy. We attempt three retraining experiments involving Malt, Brown and an in-house Berkeley-style parser and obtain a statistically significant improvement for all three parsers
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
Bare-Bones Dependency Parsing — A Case for Occam's Razor?
Proceedings of the 18th Nordic Conference of Computational Linguistics
NODALIDA 2011.
Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa.
NEALT Proceedings Series, Vol. 11 (2011), 6-11.
© 2011 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/10062/16955
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.Comment: Proceedings of ACL 201
Statistical parsing of morphologically rich languages (SPMRL): what, how and whither
The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
In this paper we present GumDrop, Georgetown University's entry at the DISRPT
2019 Shared Task on automatic discourse unit segmentation and connective
detection. Our approach relies on model stacking, creating a heterogeneous
ensemble of classifiers, which feed into a metalearner for each final task. The
system encompasses three trainable component stacks: one for sentence
splitting, one for discourse unit segmentation and one for connective
detection. The flexibility of each ensemble allows the system to generalize
well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking
(DISRPT2019
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