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2017 IJCNLP IJCNLP 2017

All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages

Abstract

AbstractWe present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

Authors

Barbara Plank

Topics

Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Machine Learning > Learning Types > Supervised Learning Machine Learning > Learning Types > Transfer Learning

Keywords

text classification multilingual nlp support vector machine word embedding character n-gram multilingual word embedding

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