Distributional Representations

We present a novel fully unsupervised al- gorithm for POS induction from plain text, motivated by the cognitive notion of proto- types. The algorithm first identifies land- mark clusters of …...

We present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits low- rank bilinear forms and …...

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations be- tween word pairs, such as hypernymy and en- tailment. We investigate a collection …...

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a pop- ular area of NLP research. …...

AbstractPrevious approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames …...

AbstractEntity normalization (or entity linking) is an important subtask of information extraction that links entity mentions in text to categories or concepts in a reference vocabulary. Machine learning based normalization …...