Burcu Can


2019

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Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Isabelle Augenstein | Spandana Gella | Sebastian Ruder | Katharina Kann | Burcu Can | Johannes Welbl | Alexis Conneau | Xiang Ren | Marek Rei
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

2018

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Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
Burcu Can | Suresh Manandhar
Computational Linguistics, Volume 44, Issue 2 - June 2018

This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.

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Characters or Morphemes: How to Represent Words?
Ahmet Üstün | Murathan Kurfalı | Burcu Can
Proceedings of The Third Workshop on Representation Learning for NLP

In this paper, we investigate the effects of using subword information in representation learning. We argue that using syntactic subword units effects the quality of the word representations positively. We introduce a morpheme-based model and compare it against to word-based, character-based, and character n-gram level models. Our model takes a list of candidate segmentations of a word and learns the representation of the word based on different segmentations that are weighted by an attention mechanism. We performed experiments on Turkish as a morphologically rich language and English with a comparably poorer morphology. The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.

2013

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Dirichlet Processes for Joint Learning of Morphology and PoS Tags
Burcu Can | Suresh Manandhar
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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An Agglomerative Hierarchical Clustering Algorithm for Labelling Morphs
Burcu Can | Suresh Manandhar
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2012

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Probabilistic Hierarchical Clustering of Morphological Paradigms
Burcu Can | Suresh Manandhar
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics