Natural Language Processing with Python

Summary

• The traditional categories of context-free grammar are atomic symbols. An important motivation for feature structures is to capture fine-grained distinctions that would otherwise require a massive multiplication of atomic categories.

• By using variables over feature values, we can express constraints in grammar productions that allow the realization of different feature specifications to be inter-dependent.

• Typically we specify fixed values of features at the lexical level and constrain the values of features in phrases to unify with the corresponding values in their children.

• Feature values are either atomic or complex. A particular subcase of atomic value is the Boolean value, represented by convention as [+/- feat].

• Two features can share a value (either atomic or complex). Structures with shared values are said to be re-entrant. Shared values are represented by numerical indexes (or tags) in AVMs.

• A path in a feature structure is a tuple of features corresponding to the labels on a sequence of arcs from the root of the graph representation.

• Two paths are equivalent if they share a value.

• Feature structures are partially ordered by subsumption. FS₀ subsumes FS₁ when FS₀ is more general (less informative) than FS₁.

• The unification of two structures FS₀ and FS₁, if successful, is the feature structure FS₂ that contains the combined information of both FS₀ and FS₁.

• If unification specializes a path π in FS, then it also specializes every path π' equivalent to π.

• We can use feature ...