[PDF] Top 20 UParse: the Edinburgh system for the CoNLL 2017 UD shared task
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UParse: the Edinburgh system for the CoNLL 2017 UD shared task
... word embeddings from Bojanowski et al. (2016) and OPUS parallel data (Tiedemann, 2012, 2009). Unless we explicitly mention in the description, we follow the same training configurations as de- scribed in Zhang et al. ... See full document
11
A Semi universal Pipelined Approach to the CoNLL 2017 UD Shared Task
... the CoNLL 2017 Shared Task, “Multilingual Parsing from Raw Text to Universal ...the system for all languages with our own fully pipelined components without rely- ing on either ... See full document
9
A Fast and Lightweight System for Multilingual Dependency Parsing
... parsing system for the CoNLL 2017 UD Shared Task, which composed of a BiLSTMs feature extractor and a MLP classi- ...Our system only uses UD version ...our ... See full document
6
IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks
... The task was to predict dependency trees from raw text. To make the ST more ac- cessible to participants, the organizers provided baseline predictions for all preprocessing steps (including word and sentence ... See full document
12
The parse is darc and full of errors: Universal dependency parsing with transition based and graph based algorithms
... treebanks. UD version ...the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et ...al., 2017). In the shared task ... See full document
8
UDPipe 2 0 Prototype at CoNLL 2018 UD Shared Task
... to CoNLL 2018 UD Shared Task, a prototype of UDPipe ...using UD treebanks as training ...Strakov´a, 2017) is used as a base- line in CoNLL 2018 UD Shared ... See full document
11
Universal Joint Morph Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task
... the CoNLL 2017 UD Shared Task on multilingual parsing from raw text to Universal De- ...our system is a joint morphological disambiguator and syntactic parser which accepts ... See full document
12
CUNI x ling: Parsing Under Resourced Languages in CoNLL 2018 UD Shared Task
... MonoTrans system (Rosa, 2017), which tries to map the source words onto similar target words; the similarity is computed based on an edit distance of the word forms, and on their frequen- cies in ... See full document
10
The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy
... the shared task, we devel- oped a new part-of-speech tagging system inspired by our previous work on MElt (Denis and Sagot, 2012), a left-to-right maximum-entropy tagger re- lying on features based ... See full document
10
Stanford’s Graph based Neural Dependency Parser at the CoNLL 2017 Shared Task
... character-based system, we trained an addi- tional set of parsers without UPOS or XPOS in- put, comparing them to the other two, with the differences graphed in Figure ...our system, provided the tagger’s ... See full document
11
The SLT Interactions Parsing System at the CoNLL 2018 Shared Task
... Strakov´a, 2017) for tokenization for almost all the treebanks except for Chinese and Japanese where we ob- served that the UDPipe segmentation had an ad- verse effect on parsing performance as opposed to gold ... See full document
7
Proceedings of the CoNLL 16 shared task
... this task, and 20 of them submitted system description ...this shared task, adopts an F1 based metric that takes into account the accuracy of identifying the senses and arguments of discourse ... See full document
10
NTHU at the CoNLL 2014 Shared Task
... Millions of non-native learners are using English as their second language (ESL) or foreign lan- guage (EFL). These learners often make different kinds of grammatical errors and are not aware of it. With a grammatical ... See full document
5
UdS at CoNLL 2013 Shared Task
... this task is not an easy job especially the number of NP using correct article is really high (more than ...this task we also adapt two approaches from (Dahlmeier et ... See full document
8
CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
... 2016 shared task that encoder-decoder architec- tures perform strongly when training data is plen- tiful, with exact-match accuracy on held-out forms surpassing 90% on many languages; we note there was a ... See full document
30
CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
... 2015 shared task (Xue et ...winning system sub- mitted by the East China Normal University (ECNU) (Wang and Lan, ...ECNU system receiving the highest score on both the WSJ development and test ... See full document
19
The University of Illinois System in the CoNLL 2013 Shared Task
... We experimented with two types of classifiers: Averaged Perceptron (AP) and an L1-generalized logistic regression classifier (LR). Since the arti- cle system is trained on the ESL data, of which we have a limited ... See full document
7
The CoNLL 2015 Shared Task on Shallow Discourse Parsing
... The discussion of learning techniques cannot be entirely separated from the use of features and the linguistic resources that are used to extract them. Standard “shallow” architectures typically make use of discrete ... See full document
16
KUNLP Grammatical Error Correction System For CoNLL 2013 Shared Task
... This paper describes an English grammat- ical error correction system for CoNLL- 2013 shared task. Error types covered by our system are article/determiner, prepo- sition, and noun ... See full document
5
POSTECH Grammatical Error Correction System in the CoNLL 2014 Shared Task
... web-based system would query a search engine with the sequences “decide among the” and “decide between the” and select the can- didate that returns the most ... See full document
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