@inproceedings{krishna-etal-2017-graph,
title = "A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in {S}anskrit",
author = "Krishna, Amrith and
Satuluri, Pavankumar and
Ponnada, Harshavardhan and
Ahmed, Muneeb and
Arora, Gulab and
Hiware, Kaustubh and
Goyal, Pawan",
editor = "Riedl, Martin and
Somasundaran, Swapna and
Glava{\v{s}}, Goran and
Hovy, Eduard",
booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2409",
doi = "10.18653/v1/W17-2409",
pages = "66--75",
abstract = "Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 {\%} to that of the entire dataset.",
}
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<abstract>Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.</abstract>
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%0 Conference Proceedings
%T A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit
%A Krishna, Amrith
%A Satuluri, Pavankumar
%A Ponnada, Harshavardhan
%A Ahmed, Muneeb
%A Arora, Gulab
%A Hiware, Kaustubh
%A Goyal, Pawan
%Y Riedl, Martin
%Y Somasundaran, Swapna
%Y Glavaš, Goran
%Y Hovy, Eduard
%S Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F krishna-etal-2017-graph
%X Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.
%R 10.18653/v1/W17-2409
%U https://aclanthology.org/W17-2409
%U https://doi.org/10.18653/v1/W17-2409
%P 66-75
Markdown (Informal)
[A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit](https://aclanthology.org/W17-2409) (Krishna et al., TextGraphs 2017)
ACL