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Universal Dependency Annotation for Multilingual Parsing

TLDR
A new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean is presented, made freely available in order to facilitate research on multilingual dependency parsing.
Abstract
We present a new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean. To show the usefulness of such a resource, we present a case study of crosslingual transfer parsing with more reliable evaluation than has been possible before. This ‘universal’ treebank is made freely available in order to facilitate research on multilingual dependency parsing. 1

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Citations
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Proceedings ArticleDOI

Improving Pre-Trained Multilingual Model with Vocabulary Expansion

TL;DR: This paper investigated two approaches (i.e., joint mapping and mixture mapping) based on a pre-trained multilingual model BERT for addressing the OOV problem on a variety of tasks, including part-of-speech tagging, named entity recognition, machine translation quality estimation, and machine reading comprehension.
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Multilingual Irony Detection with Dependency Syntax and Neural Models

TL;DR: An in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective suggests that fine-grained dependency- based syntactic information is informative for the detection of irony.
Book ChapterDOI

Automatic Identification and Disambiguation of Concepts and Named Entities in the Multilingual Wikipedia

TL;DR: An automatic multilingual annotation of the Wikipedia dumps in two languages, with both word senses and named entities is presented, using Babelfy 1.0, a state-of-the-art multilingual Word Sense Disambiguation and Entity Linking system.
DissertationDOI

On understanding character-level models for representing morphology

Clara Vania
TL;DR: This thesis presents a careful, in-depth study and analyses of character-level models and their relation to morphology, providing insights and future research directions on building morphologically-aware computational NLP models.
Journal ArticleDOI

Universal Derivations 1.0, A Growing Collection of Harmonised Word-Formation Resources

TL;DR: The paper deals with harmonisation of existing data resources containing word-formation features by converting them into a common file format and partially aligning their annotation schemas, resulting in the resulting “Universal Derivations 1.0” collection.
References
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ReportDOI

Building a large annotated corpus of English: the penn treebank

TL;DR: As a result of this grant, the researchers have now published on CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, which includes a fully hand-parsed version of the classic Brown corpus.
Proceedings ArticleDOI

Accurate Unlexicalized Parsing

TL;DR: It is demonstrated that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar.
Proceedings Article

Generating Typed Dependency Parses from Phrase Structure Parses

TL;DR: A system for extracting typed dependency parses of English sentences from phrase structure parses that captures inherent relations occurring in corpus texts that can be critical in real-world applications is described.
Proceedings ArticleDOI

CoNLL-X Shared Task on Multilingual Dependency Parsing

TL;DR: How treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured is described and general conclusions about multi-lingual parsing are drawn.
Proceedings ArticleDOI

The Stanford Typed Dependencies Representation

TL;DR: This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding, and considers the underlying design principles of the Stanford scheme.
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