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MaltParser is a system for data-driven dependency parsing, which can be used to induce a parsing model from treebank data and to parse new data using an induced model.
MaltParser has the possibility of distinguishing between different kinds of null-values when extracting the feature
vector. For input columns like POSTAG it is possible to differentiate two null-values:
NO NODE: There exists no corresponding dependency graph node (e.g., because the lookahead extend beyond the end of the string),
which means that the feature is really undefined.
ROOT NODE: The dependency graph node is a root node, which means that it is not possible to extract an input column value
(for example, the word form or the part-of-speech).
In addition to the two null value categories for input columns, there is one more for the output columns:
NO VALUE: The dependency graph node exists and is not the root, but has not yet been assigned a value for the output column
requested (e.g., has not been assigned a head and therefore does not have a dependency type).
With the option --singlemalt-null_value it is possible to specify the degree of differentiation of null-values.
none: Excludes all kinds of null-values when extracting the feature vector, this option value is not possible for learning
methods that have symbolic feature vector encoding.
one: Maps all kinds of null values to one symbol.
rootlabel: Maps all kinds of null values to one symbol and for output columns this symbol is the same as the root
label (used to emulate MaltParser 0.4)
rootnode: Distinguishes between NO NODE and ROOT NODE, and the NO VALUE null-value case is mapped
to the ROOT NODE null-value for output columns.
novalue: Distinguishes between NO NODE and ROOT NODE for both input and output columns, and NO VALUE for output columns.