The AI-KU system at the SPMRL 2013 shared task: unsupervised features for dependency parsing

Abstract

We propose the use of the word categories and embeddings induced from raw text as auxiliary features in dependency parsing. To induce word features, we make use of contextual, morphologic and orthographic properties of the words. To exploit the contextual information, we make use of substitute words, the most likely substitutes for target words, generated by using a statistical language model. We generate morphologic and orthographic properties of word types in an unsupervised manner. We use a co-occurrence model with these properties to embed words onto a 25-dimensional unit sphere. The AI-KU system shows improvements for some of the languages it is trained on for the first Shared Task of Statistical Parsing of Morphologically Rich Languages.