Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks

Abhisek Chakrabarty, Onkar Arun Pandit, Utpal Garain


Abstract
We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures - the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are - (i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages - Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset (having 1,702 sentences with a total of 20,257 word tokens), which is an additional contribution of this work.
Anthology ID:
P17-1136
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1481–1491
Language:
URL:
https://www.aclweb.org/anthology/P17-1136
DOI:
10.18653/v1/P17-1136
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1136.pdf
Software:
 P17-1136.Software.zip
Dataset:
 P17-1136.Datasets.zip