Supervised Morphological Segmentation Using Rich Annotated Lexicon

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405556" target="_blank" >RIV/00216208:11320/19:10405556 - isvavai.cz</a>

Result on the web

<a href="http://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdf" target="_blank" >http://lml.bas.bg/ranlp2019/proceedings-ranlp-2019.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Supervised Morphological Segmentation Using Rich Annotated Lexicon

Original language description

Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning based approaches for the morphological segmentation task. We trained our models using annotated segmentation lexicons. To evaluate the effect of the training data size on our models, we decided to create a large hand-annotated morphologically segmented corpus of Persian words, which is, to the best of our knowledge, the first and the only segmentation lexicon for the Persian language. In the experimental phase, using the hand-annotated Persian lexicon and two smaller similar lexicons for Czech and Finnish languages, we evaluated the effect of the training data size, different hyper-parameters settings as well as different RNN-based models.

Czech name

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Czech description

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Type

D - Article in proceedings

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Article name in the collection

International Conference &quot;Recent Advances in Natural Language Processing&quot;

ISBN

978-954-452-055-7

ISSN

1313-8502

e-ISSN

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Number of pages

10

Pages from-to

52-61

Publisher name

INCOMA Ltd.

Place of publication

Varna, Bulgaria

Event location

Varna, Bulgaria

Event date

Sep 2, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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