Phrase Representations for Multiword Expressions

Recent works in Natural Language Processing (NLP) using neural networks have focused on learning dense word representations to perform classification tasks. When dealing with phrase prediction problems, is is common practice to use special tagging schemes to identify segments boundaries. This allows these tasks to be expressed as common word tagging problems. In this paper, we propose to learn fixed-size representations for arbitrarily sized chunks. We introduce a model that takes advantage of such representations to perform phrase tagging by directly identifying and classifying phrases. We evaluate our approach on the task of multiword expression (MWE) tagging and show that our model outperforms the stateof-the-art model for this task.