Jean David Ruvini

Also published as: Jean-David Ruvini


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Neural Network based Extreme Classification and Similarity Models for Product Matching
Kashif Shah | Selcuk Kopru | Jean-David Ruvini
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.

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Learning Better Internal Structure of Words for Sequence Labeling
Yingwei Xin | Ethan Hart | Vibhuti Mahajan | Jean-David Ruvini
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Character-based neural models have recently proven very useful for many NLP tasks. However, there is a gap of sophistication between methods for learning representations of sentences and words. While, most character models for learning representations of sentences are deep and complex, models for learning representations of words are shallow and simple. Also, in spite of considerable research on learning character embeddings, it is still not clear which kind of architecture is the best for capturing character-to-word representations. To address these questions, we first investigate the gaps between methods for learning word and sentence representations. We conduct detailed experiments and comparisons on different state-of-the-art convolutional models, and also investigate the advantages and disadvantages of their constituents. Furthermore, we propose IntNet, a funnel-shaped wide convolutional neural architecture with no down-sampling for learning representations of the internal structure of words by composing their characters from limited, supervised training corpora. We evaluate our proposed model on six sequence labeling datasets, including named entity recognition, part-of-speech tagging, and syntactic chunking. Our in-depth analysis shows that IntNet significantly outperforms other character embedding models and obtains new state-of-the-art performance without relying on any external knowledge or resources.


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Distributed Word Representations Improve NER for e-Commerce
Mahesh Joshi | Ethan Hart | Mirko Vogel | Jean-David Ruvini
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing


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Structuring E-Commerce Inventory
Karin Mauge | Khash Rohanimanesh | Jean-David Ruvini
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)