@inproceedings{boullier-2003-supertagging,
    title = "Supertagging: A Non-Statistical Parsing-Based Approach",
    author = "Boullier, Pierre",
    booktitle = "Proceedings of the Eighth International Conference on Parsing Technologies",
    month = apr,
    year = "2003",
    address = "Nancy, France",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W03-3006/",
    pages = "55--65",
    abstract = "We present a novel approach to supertagging w.r.t. some lexicalized grammar G. It differs from previous approaches in several ways:- These supertaggers rely only on structural information: they do not need any training phase;- These supertaggers do not compute the ``best{``} supertag for each word, but rather a set of supertags. These sets of supertags do not exclude any supertag that will eventually be used in a valid complete derivation (i.e., we have a recall score of 100{\%});- These supertaggers are in fact true parsers which accept supersets of L(G) that can be more efficiently parsed than the sentences of L(G)."
}