IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.
Code (0)
등록된 구현이 없습니다.
Tasks
ARCDependency ParsingMorphological TaggingPart-Of-Speech TaggingSentenceSentence segmentationSimilar Papers 제목 키워드 기반
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later
This paper describes the University of Geneva{'}s submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is…
Dependency ParsingFeature EngineeringWord EmbeddingsCoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task
In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Compared to the last year, this year the shared task includes two new main metri…
Dependency ParsingLemmatizationMachine TranslationMorphological Tagging+1