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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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hr500k – A Reference Training Corpus of Croatian.

TL;DR: The hr500k as mentioned in this paper reference training corpus is a large-scale corpus of 500,000 tokens, segmented at document, sentence and word level and annotated for morphosyntax, lemmas, dependency syntax, named entities, and semantic roles.
Proceedings ArticleDOI

Reconstruction of Word Embeddings from Sub-Word Parameters

TL;DR: Before task-specific training, this work first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures to benefit from this resource without paying the cost by operating strictly at the sub-lexical level.
Posted Content

Left-corner Methods for Syntactic Modeling with Universal Structural Constraints

TL;DR: It is concluded that left-corner methods are particularly useful for syntax-oriented systems, as it can exploit efficiently the inherent universal constraints in languages.
Proceedings ArticleDOI

The Impact of Data Challenges on Intent Detection and Slot Filling for the Home Assistant Scenario

TL;DR: This paper systematically generates datasets in the Romanian language that model these data complexities and investigates how well two of the most prominent tools – Wit.ai and Rasa NLU – solve the tasks of intent detection and slot filling, given the considered data complexities.
Proceedings ArticleDOI

Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data

Fahad AlGhamdi, +1 more
TL;DR: This paper explores leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data and shows that the proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.
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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