András Simonyi


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Bilingual Transfer Learning for Online Product Classification
Erik Lehmann | András Simonyi | Lukas Henkel | Jörn Franke
Proceedings of Workshop on Natural Language Processing in E-Commerce

Consumer Price Indices (CPIs) are one of the major statistics produced by Statistical Offices, and of crucial importance to Central Banks. To calculate CPIs, statistical offices collect a large amount of individual prices of goods and services. Nowadays prices of many consumer goods can be obtained online, enabling a much more detailed measurement of inflation rates. One major challenge is to classify the variety of products, from different shops and languages into the given statistical schema consisting of a complex multi-level classification hierarchy - the European Classification of Individual Consumption according to Purpose (ECOICOP) for European countries, since there is no model, mapping or labelled data available. We focus in our analysis on food, beverage and tobacco which account for 74 of the 258 ECOICOP categories and 19 % of the Euro Area inflation basket. In this paper we build a classifier on web scraped, hand-labeled product data from German retailers and test the transfer to French data using cross lingual word embedding. We compare its performance against a classifier trained on the single languages and a classifier with both languages trained jointly. Furthermore, we propose a pipeline to effectively create a data set with balanced labels using transferred predictions and active learning. In addition we test how much data it takes to build a single language classifier from scratch an if there are benefits from multilingual training. Our proposed system reduces the time to complete the task by about two thirds and is already used to support the analysis of inflation.


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What’s Wrong, Python? – A Visual Differ and Graph Library for NLP in Python
Balázs Indig | András Simonyi | Noémi Ligeti-Nagy
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Mapping Ontologies Using Ontologies: Cross-lingual Semantic Role Information Transfer
Balázs Indig | Márton Miháltz | András Simonyi
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the process of enriching the verb frame database of a Hungarian natural language parser to enable the assignment of semantic roles. We accomplished this by linking the parser’s verb frame database to existing linguistic resources such as VerbNet and WordNet, and automatically transferring back semantic knowledge. We developed OWL ontologies that map the various constraint description formalisms of the linked resources and employed a logical reasoning device to facilitate the linking procedure. We present results and discuss the challenges and pitfalls that arose from this undertaking.