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HPC needs for Integrated Hydrological Models: examples of application of the GEOtop model to the Vienna Scientific Cluster

Abstract for the Austrian HPC Meeting 2017 - AHPC17

Giacomo Bertoldi, Samuel Senoner

Turkish National Flag. (Türk Bayrağı)

Turkish national flag with TiKz.

Ali İhsan Çanakoğlu

COTAI report template

A template for student reports at COTAI. Center of Talent in AI (COTAI) was founded in Sept. 2019 to promote talent training for AI research and development. Reach us at www.cot.ai and contact@cot.ai

Minh Nguyen - AI intern at COTAI

Curriculum Vitae

Latex CV Template by Md Nazmus Sakib Miazi Email: mmiazi@gmail.com

Md Nazmus Sakib Miazi

Multi-Tagging for Transition-based Dependency Parsing

This project focuses on a modification of a greedy transition based dependency parser. Typically a Part-Of-Speech (POS) tagger models a probability distribution over all the possible tags for each word in the given sentence and chooses one as its best guess. This is then pass on to the parser which uses this information to build a parse tree. The current state of the art for POS tagging is about 97% word accuracy, which seems high but results in a around 56% sentence accuracy. Small errors at the POS tagging phase can lead to large errors down the NLP pipeline and transition based parsers are particularity sensitive to these types of mistakes. A maximum entropy Markov model was trained as a POS multi-tagger passing more than its 1-best guess to the parser which was thought could make a better decision when committing to a parse for the sentence. This has been shown to give improved accuracy in other parsing approaches. We shown there is a correlation between tagging ambiguity and parsers accuracy and in fact the higher the average tags per word the higher the accuracy.

awhillas