Giuseppe Di Fabbrizio

Also published as: Giuseppe Fabbrizio


2017

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Web-Scale Language-Independent Cataloging of Noisy Product Listings for E-Commerce
Pradipto Das | Yandi Xia | Aaron Levine | Giuseppe Di Fabbrizio | Ankur Datta
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

The cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding. However, manual and rule based approaches to categorization are not scalable. In this paper, we compare several classifiers for categorizing listings in both English and Japanese product catalogs. We show empirically that a combination of words from product titles, navigational breadcrumbs, and list prices, when available, improves results significantly. We outline a novel method using correspondence topic models and a lightweight manual process to reduce noise from mis-labeled data in the training set. We contrast linear models, gradient boosted trees (GBTs) and convolutional neural networks (CNNs), and show that GBTs and CNNs yield the highest gains in error reduction. Finally, we show GBTs applied in a language-agnostic way on a large-scale Japanese e-commerce dataset have improved taxonomy categorization performance over current state-of-the-art based on deep belief network models.

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Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models
Yandi Xia | Aaron Levine | Pradipto Das | Giuseppe Di Fabbrizio | Keiji Shinzato | Ankur Datta
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories. Compared to a state-of-the-art Gradient Boosted Tree (GBT) classifier, the proposed model reduces training time from three weeks to three days while maintaining more than 96% accuracy. Additionally, our proposed model characterizes products by imputing attentive focus on word tokens in a language agnostic way. The attention words have been observed to be semantically highly correlated with the predicted categories and give us a choice of automatic feature extraction for downstream processing.

2015

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Comment-to-Article Linking in the Online News Domain
Ahmet Aker | Emina Kurtic | Mark Hepple | Rob Gaizauskas | Giuseppe Di Fabbrizio
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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A Hybrid Approach to Multi-document Summarization of Opinions in Reviews
Giuseppe Di Fabbrizio | Amanda Stent | Robert Gaizauskas
Proceedings of the 8th International Natural Language Generation Conference (INLG)

2012

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Building Text-To-Speech Voices in the Cloud
Alistair Conkie | Thomas Okken | Yeon-Jun Kim | Giuseppe Di Fabbrizio
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The AT&T VoiceBuilder provides a new tool to researchers and practitioners who want to have their voices synthesized by a high-quality commercial-grade text-to-speech system without the need to install, configure, or manage speech processing software and equipment.It is implemented as a web service on the AT&T Speech Mashup Portal.The system records and validates users' utterances, processes them to build a synthetic voice and provides a web service API to make the voice available to real-time applications through a scalable cloud-based processing platform. All the procedures are automated to avoid human intervention. We present experimental comparisons of voices built using the system.

2010

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Emotion Detection in Email Customer Care
Narendra Gupta | Mazin Gilbert | Giuseppe Di Fabbrizio
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text

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Capturing the Stars: Predicting Ratings for Service and Product Reviews
Narendra Gupta | Giuseppe Di Fabbrizio | Patrick Haffner
Proceedings of the NAACL HLT 2010 Workshop on Semantic Search

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Have2eat: a Restaurant Finder with Review Summarization for Mobile Phones
Giuseppe Fabbrizio | Narendra Gupta | Sveva Besana | Premkumar Mani
Coling 2010: Demonstrations

2008

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Referring Expression Generation Using Speaker-based Attribute Selection and Trainable Realization (ATTR)
Giuseppe Di Fabbrizio | Amanda J. Stent | Srinivas Bangalore
Proceedings of the Fifth International Natural Language Generation Conference

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Trainable Speaker-Based Referring Expression Generation
Giuseppe Di Fabbrizio | Amanda Stent | Srinivas Bangalore
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2006

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Learning the Structure of Task-Driven Human-Human Dialogs
Srinivas Bangalore | Giuseppe Di Fabbrizio | Amanda Stent
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Data Driven Approach to Relevancy Recognition for Contextual Question Answering
Fan Yang | Junlan Feng | Giuseppe Di Fabbrizio
Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006