Detecting syntactic differences automatically using the Minimum Description Length principle

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10427023" target="_blank" >RIV/00216208:11320/20:10427023 - isvavai.cz</a>

Result on the web

<a href="https://www.clips.uantwerpen.be/clinjournal/clinj/article/view/109" target="_blank" >https://www.clips.uantwerpen.be/clinjournal/clinj/article/view/109</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Detecting syntactic differences automatically using the Minimum Description Length principle

Original language description

In this paper we present a systematic approach to detect and rank hypotheses about possible syntactic differences for further investigation by leveraging parallel data and using the Minimum Description Length (MDL) principle. We deploy the SQS-algorithm (‘Summarising event seQuenceS’; Tatti and Vreeken 2012) – an MDL-based algorithm – to mine ‘typical’ sequences of Part of Speech (POS) tags for each language under investigation. We create a shortlist of potential syntactic differences based on the number of parallel sentences with a mismatch in pattern occurrence. We applied our method to parallel corpora of English, Dutch and Czech sentences from the Europarl v7 corpus (Koehn 2005). The approach proved useful in both retrieving POS building blocks of a language as well as pointing to meaningful syntactic differences between languages. Despite a clear sensitivity to tagging accuracy, our results and approach are promising

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů