However, the technique is inherently approximate: it will return a lower probability parse under the parsing model if a higher probability parse can only be constructed from a supertag sequence returned by a subsequent iteration.

An obvious way to exploit this without being bound by its decisions is to incorporate these features directly into the parsing model .

We apply both techniques to our integrated supertagging and parsing model .

Our parsing model is also a distribution over variables Ti, along with an additional quadratic number of span(i, j ) variables.

Digging deeper, we compared parser model score against Viterbi F—score and oracle F-score at a va-

The parsing model can be defined as a conditional distribution p(y|x; w) over each projective parse tree 3/ for a particular sentence X, parameterized by a vector w. The probability of a parse tree is

By leveraging some assistant data, the dependency parsing model can directly utilize the additional information to capture the word-to-word level relationships.

If both PMI features exist and PMIW-thwit, bat) > PMIW-thwall, bat), indicating to our dependency parsing model that the preposition word with depends on the verb hit is a good choice.

Web-derived selectional preference features based on PMI values are trickier to incorporate into the dependency parsing model because they are continuous rather than discrete.

Log-linear dependency parsing model is sensitive to inappropriately scaled feature.