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        <title>Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task</title>
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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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            <namePart type="given">Dan</namePart>
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    <abstract>We present the Open University’s submission to the CoNLL 2017 Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires a lattice as input, so we generate morphological analyses of surface tokens using a data-driven morphological analyzer that derives its lexicon from the UD training corpora, and we rely on UDPipe for sentence segmentation and surface-level tokenization. We report our official macro-average LAS is 56.56. Although our model is not as performant as many others, it does not make use of neural networks, therefore we do not rely on word embeddings or any other data source other than the corpora themselves. In addition, we show the utility of a lexicon-backed morphological analyzer for the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD community should define a UD-compatible standard for access to lexical resources, which we argue is crucial for MRLs and low resource languages in particular.</abstract>
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    <identifier type="doi">10.18653/v1/K17-3027</identifier>
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%0 Conference Proceedings
%T Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task
%A More, Amir
%A Tsarfaty, Reut
%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 more-tsarfaty-2017-universal
%X We present the Open University’s submission to the CoNLL 2017 Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires a lattice as input, so we generate morphological analyses of surface tokens using a data-driven morphological analyzer that derives its lexicon from the UD training corpora, and we rely on UDPipe for sentence segmentation and surface-level tokenization. We report our official macro-average LAS is 56.56. Although our model is not as performant as many others, it does not make use of neural networks, therefore we do not rely on word embeddings or any other data source other than the corpora themselves. In addition, we show the utility of a lexicon-backed morphological analyzer for the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD community should define a UD-compatible standard for access to lexical resources, which we argue is crucial for MRLs and low resource languages in particular.
%R 10.18653/v1/K17-3027
%U https://aclanthology.org/K17-3027
%U https://doi.org/10.18653/v1/K17-3027
%P 253-264

Markdown (Informal)

[Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task](https://aclanthology.org/K17-3027) (More & Tsarfaty, CoNLL 2017)

• Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task (More & Tsarfaty, CoNLL 2017)

ACL

• Amir More and Reut Tsarfaty. 2017. Universal Joint Morph-Syntactic Processing: The Open University of Israel’s Submission to The CoNLL 2017 Shared Task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 253–264, Vancouver, Canada. Association for Computational Linguistics.