SemEval-2007 task 09

Màrquez, Lluı́s; Villarejo, Lluís; Martí, M. Antònia; Taulé, Mariona

doi:10.3115/1621474.1621482

Cited by 19 publications

(1 citation statement)

References 2 publications

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“…All resulting layers are independent of each other, thus making easier the data management. The initial annotation was performed manually for syntax, semiautomatically in the case of arguments and thematic roles, and fully automatically for PoS Màrquez et al, 2007).…”

Section: Catalan and Spanishmentioning

confidence: 99%

The CoNLL-2009 shared task

Hajič

Ciaramita

Johansson

et al. 2009

Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09

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191

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This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirec-tional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants , our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 tree-banks).

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Section: Catalan and Spanishmentioning

confidence: 99%

The CoNLL-2009 shared task

Hajič

Ciaramita

Johansson

et al. 2009

Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09

Self Cite

191

View full text Add to dashboard Cite

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Discovering Semantic Relations Within Nominals

Colhon

Cristea

Gîfu

2016

Communications in Computer and Information Science

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Structured Named Entity Recognition by Cascading CRFs

Dupont

Dinarelli

Lautier³

2018

Computational Linguistics and Intelligent Text Processing

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NER is an important task in NLP, often used as a basis for further treatments. A new challenge has emerged in the last few years: structured named entity recognition, where not only named entities must be identied but also their hierarchical components. In this article, we describe a cascading CRFs approach to address this challenge. It reaches the state of the art while remaining very simple on a structured NER challenge. We then oer an error analysis of our system based on a detailed, yet simple, error classication.

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SemEval-2007 task 09

Cited by 19 publications

References 2 publications

The CoNLL-2009 shared task

The CoNLL-2009 shared task

Discovering Semantic Relations Within Nominals

Structured Named Entity Recognition by Cascading CRFs

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