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        <title>A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task</title>
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        <namePart type="given">Xian</namePart>
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            <title>Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies</title>
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            <namePart type="given">Jan</namePart>
            <namePart type="family">Hajič</namePart>
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    <abstract>For this year’s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn low dimensional dense features using non-linear classifier, our system uses structured linear classifiers to learn millions of sparse features. Specifically, we trained a linear classifier for sentence boundary prediction, linear chain conditional random fields (CRFs) for tokenization, part-of-speech tagging and morph analysis. A second order graph based parser learns the tree structure (without relations), and fa linear tree CRF then assigns relations to the dependencies in the tree. Our system achieves reasonable performance – 67.87% official averaged macro F1 score</abstract>
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%0 Conference Proceedings
%T A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task
%A Qian, Xian
%A Liu, Yang
%Y Hajič, Jan
%Y Zeman, Dan
%S Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qian-liu-2017-non
%X For this year’s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn low dimensional dense features using non-linear classifier, our system uses structured linear classifiers to learn millions of sparse features. Specifically, we trained a linear classifier for sentence boundary prediction, linear chain conditional random fields (CRFs) for tokenization, part-of-speech tagging and morph analysis. A second order graph based parser learns the tree structure (without relations), and fa linear tree CRF then assigns relations to the dependencies in the tree. Our system achieves reasonable performance – 67.87% official averaged macro F1 score
%R 10.18653/v1/K17-3015
%U https://aclanthology.org/K17-3015/
%U https://doi.org/10.18653/v1/K17-3015
%P 143-151

Markdown (Informal)

[A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task](https://aclanthology.org/K17-3015/) (Qian & Liu, CoNLL 2017)

• A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task (Qian & Liu, CoNLL 2017)

ACL

• Xian Qian and Yang Liu. 2017. A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 143–151, Vancouver, Canada. Association for Computational Linguistics.