@article{TACL586,
        author = {Mo Yu and Mark Dredze},
        title = {Learning Composition Models for Phrase Embeddings},
        journal = {Transactions of the Association for Computational Linguistics},
        volume = {3},
        year = {2015},
        keywords = {},
        abstract = {Lexical embeddings can serve as useful representations for
words for a variety of NLP tasks, but learning embeddings for phrases can be
challenging. While separate embeddings are learned for each word, this is
infeasible for every phrase. We construct phrase embeddings by learning how
to compose word embeddings using features that capture phrase structure and
context. We propose efficient unsupervised and task-specific learning
objectives that scale our model to large datasets. We demonstrate
improvements on both language modeling and several phrase semantic
similarity tasks with various phrase lengths. We make the implementation of
our model and the datasets available for general use.},
        issn = {2307-387X},
        url =
{https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/586},
        pages = {227--242}
}