Ali Basirat


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Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment
Hartger Veeman | Marc Allassonnière-Tang | Aleksandrs Berdicevskis | Ali Basirat
Proceedings of the 24th Conference on Computational Natural Language Learning

Grammatical gender is assigned to nouns differently in different languages. Are all factors that influence gender assignment idiosyncratic to languages or are there any that are universal? Using cross-lingual aligned word embeddings, we perform two experiments to address these questions about language typology and human cognition. In both experiments, we predict the gender of nouns in language X using a classifier trained on the nouns of language Y, and take the classifier’s accuracy as a measure of transferability of gender systems. First, we show that for 22 Indo-European languages the transferability decreases as the phylogenetic distance increases. This correlation supports the claim that some gender assignment factors are idiosyncratic, and as the languages diverge, the proportion of shared inherited idiosyncrasies diminishes. Second, we show that when the classifier is trained on two Afro-Asiatic languages and tested on the same 22 Indo-European languages (or vice versa), its performance is still significantly above the chance baseline, thus showing that universal factors exist and, moreover, can be captured by word embeddings. When the classifier is tested across families and on inanimate nouns only, the performance is still above baseline, indicating that the universal factors are not limited to biological sex.


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From Raw Text to Universal Dependencies - Look, No Tags!
Miryam de Lhoneux | Yan Shao | Ali Basirat | Eliyahu Kiperwasser | Sara Stymne | Yoav Goldberg | Joakim Nivre
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macro-averaged LAS F1 of 65.11 in the official test run, which improved to 70.49 after bug fixes. We obtained the 2nd best result for sentence segmentation with a score of 89.03.

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Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
Ali Basirat | Joakim Nivre
Proceedings of the 21st Nordic Conference on Computational Linguistics


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Automatic Enhancement of LTAG Treebank
Farzaneh Zarei | Ali Basirat | Heshaam Faili | Maryam Sadat Mirian
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013


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Constructing Linguistically Motivated Structures from Statistical Grammars
Ali Basirat | Heshaam Faili
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011