bot.zen @ EVALITA 2016 - A minimally-deep learning PoS-tagger (trained for Italian Tweets)

Abstract

This article describes the system that participated in the POS tagging for Italian Social Media Texts (PoSTWITA) task of the 5th periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language EVALITA 2016. The work is a continuation of Stemle (2016) with minor modifications to the system and different data sets. It combines a small assertion of trending techniques, which implement matured methods, from NLP and ML to achieve competitive results on PoS tagging of Italian Twitter texts; in particular, the system uses word embeddings and character-level representations of word beginnings and endings in a LSTM RNN architecture. Labelled data (Italian UD corpus, DiDi and PoSTWITA) and unlabbelled data (Italian C4Corpus and PAISA’) were used for training. The system is available under the APLv2 open-source license.

Publication
Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016)
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