166 research outputs found
Ensemble Parsing and its Effect on Machine Translation
The focus of much of dependency parsing is on creating new modeling techniques and examining new feature sets for existing dependency models. Often these new models are lucky to achieve equivalent results with the current state of
the art results and often perform worse. These approaches are for languages that are often resource-rich and have ample training data available for dependency parsing. For this reason, the accuracy scores are often quite high. This, by its very nature, makes it quite difficult to create a significantly large increase in the current state-of-the-art. Research in this area is often concerned with small accuracy changes or very specific localized changes, such as increasing accuracy of a particular linguistic construction. With so many modeling techniques available to languages with large resources the problem exists on how to exploit the current techniques with the use of combination, or ensemble, techniques along with this plethora of data.
Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. The structures may have more complexity due to the depth of their co-
ordination or noun phrases. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. The types of parsing errors that cause significant
problems in other NLP applications is currently an unknown
Cross-lingual Coreference Resolution of Pronouns
This work is, to our knowledge, a first attempt at a machine learning approach to cross-lingual
coreference resolution, i.e. coreference resolution (CR) performed on a bitext. Focusing on CR of English pronouns, we leverage language differences and enrich the feature set of a standard monolingual CR system for English with features extracted from the Czech side of the bitext. Our work also includes a supervised pronoun aligner that outperforms a GIZA++ baseline in terms of both intrinsic evaluation and evaluation on CR. The final cross-lingual CR system has successfully outperformed both a monolingual CR and a cross-lingual projection system
Translation of "It" in a Deep Syntax Framework
We present a novel approach to the translation of the English personal pronoun it to Czech. We conduct a linguistic analysis on how the distinct categories of it are usually mapped to their Czech counterparts. Armed with these observations, we design a discriminative translation model of it, which is then integrated into the TectoMT deep syntax MT framework. Features in the model take advantage of rich syntactic annotation TectoMT is based on, external
tools for anaphoricity resolution, lexical co-occurrence frequencies measured on a large parallel corpus and gold coreference annotation. Even though the new model for it exhibits no improvement in terms of BLEU, manual evaluation shows that it outperforms the original solution in
8.5% sentences containing it
Grammatical number of nouns in Czech: linguistic theory and treebank annotation
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 211-222.
© 2010 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/10062/15891
Two Case Studies on Translating Pronouns in a Deep Syntax Framework
We focus on improving the translation of the English pronoun it and English reflexive pronouns in an English-Czech syntax-based machine translation framework. Our evaluation both from intrinsic and extrinsic perspective shows that adding specialized syntactic and coreference-related features leads to an improvement in trans-
lation quality
Planting Trees in the Desert: Delexicalized Tagging and Parsing Combined
Various unsupervised and semi-supervised
methods have been proposed to tag and parse
an unseen language. We explore delexicalized
parsing, proposed by (Zeman and Resnik,
2008), and delexicalized tagging, proposed
by (Yu et al., 2016). For both approaches
we provide a detailed evaluation on Universal
Dependencies data (Nivre et al., 2016), a de-facto standard for multi-lingual morphosyntactic processing (while the previous work used other datasets). Our results confirm that in separation, each of the two delexicalized techniques has some limited potential when no annotation of the target language is available. However, if used in combination, their errors multiply beyond acceptable limits. We demonstrate that even the tiniest bit of expert annotation in the target language may contain significant potential and should be used if available
Parsing Aided by Intra-Clausal Coordination Detection
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), 79-84.
© 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
Formemes in English-Czech Deep Syntactic MT
One of the most notable recent improvements of the TectoMT English-to-Czech translation is a systematic and theoretically supported revision of formemes—the annotation of morpho-syntactic features of content words in deep dependency syntactic structures based on the Prague tectogrammatics theory. Our modifications aim at reducing data sparsity, increasing consistency across languages and widening the usage area of this markup. Formemes can be used not only in MT, but in various other NLP tasks
HamleDT 2.0: Thirty Dependency Treebanks Stanfordized
We present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently.
We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes. We describe both of the annotation styles, including adjustments that were necessary to make, and provide details about the conversion process. We also discuss the differences between the two styles, evaluating their advantages and disadvantages, and note the effects of the differences on the conversion.
We regard the stanfordization as generally successful, although we admit several shortcomings, especially in the distinction between direct and indirect objects, that have to be addressed in future.
We release part of HamleDT 2.0 freely; we are not allowed to redistribute the whole dataset, but we do provide the conversion pipeline
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