Supertagging: A Non-Statistical Parsing-Based Approach

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).