Part of Speech Tagging for French Social Media Data

摘要

In the context of Social Media Analytics, Natural Language Processing tools face new challenges on on-line conversational text, such as microblogs, chat, or text messages, because of the specificity of the language used in these channels. This work addresses the problem of Part-Of-Speech tagging (initially for French but also for English) on noisy language usage from the popular social media services like Twitter, Facebook and forums. We employ a linear-chain conditional random fields (CRFs) model, enriched with several morphological, orthographic, lexical and large-scale word clustering features. Our experiments used different feature configurations to train the model. We achieved a higher tagging performance with these features, compared to baseline results on French social media bank. Moreover, experiments on English social media content show that our model improves over previous works on these data.