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Encoding DAGs

  The syntax of DATR, like its name and its minimalist philosophy, owes more than a little to that of the unification grammar language PATR (Shieber 1986). With hindsight this may have been a bad design decision since similarity of syntax tends to imply a similarity of semantics. And, as we shall see below, and elsewhere, there is a subtle but important semantic difference.

As a feature-based formalism with a syntax modelled on PATR, it would be reasonable to expect that DATR can be used to describe directed acyclic graphs (DAGs) in a PATR-like fashion. Consider an example such as the following:

    DAG1:
        <vp agr> == <v agr>
        <v agr per> == 3
        <vp agr gen> == masc.
This looks like simple reentrancy from which we would expect to be able to infer:
    DAG1:
        <vp agr per> = 3.
And, indeed, this turns out to be valid. But matters are not as simple as the example makes them appear: if DAG1 was really the DAG it purports to be, then we would also expect to be able to infer:
    DAG1:
        <v agr gen> = masc.
But this is not valid, in fact <v agr gen> is undefined. It might be tempting to conclude from this that the equality operator in DATR is very different from the corresponding operator in PATR, but this would be to misunderstand what has happened in this example. In fact, the semantics of the statement
    DAG1:
        <vp agr> == <v agr>.
taken in isolation is very similar to the semantics of the corresponding PATR statement: both assert equality of values associated with the two paths. The DATR statement is slightly weaker in that it allows the left-hand-side to be defined when the right-hand-side is undefined. But, even in DATR, if both sides are defined they must be the same, so, in principle, the value of the left-hand-side does semantically constrain the value of the right-hand-side. However, in a DATR description, specifying explicit values for extensions of the left-hand-side of such an equality constraint overrides its effect, and thus does not influence the values on its right-hand-side.

Another difference lies in the fact that DATR subpaths and superpaths can have values of their own:

    DAG2:
        <v agr> == sing
        <v agr per> == 3.
From this little description we can derive the following statements, inter alia:

    DAG2:
        <v agr> = sing
        <v agr num> = sing
        <v agr per> = 3
        <v agr per num> = 3.
From the perspective of a standard untyped DAG-encoding language like PATR, this is strange. In PATR, if <v agr per> has value 3, then neither <v agr> nor <v agr per num> can have (atomic) values.

As these examples clearly show, DATR descriptions do not map trivially into (sets of) standard DAGs (although neither are they entirely dissimilar). But that does not mean that DATR descriptions cannot describe standard DAGs. Indeed, there are a variety of ways in which this can be done. An especially simple approach is possible when the DAGs one is interested are all built out of a set of paths whose identity is known in advance (Kilbury et al. 1991). In this case, we can use DATR paths as DAG paths, more or less directly:

    PRONOUN2:
        <referent> == '<' 'NP' referent '>'.
    She2:
        <> == PRONOUN2
        <case> == nominative
        <person> == third
        <number> == singular.
From this description, we can derive the following theorems:

    She2:
        <case> = nominative
        <person> = third
        <number> = singular
        <referent> = < NP referent >.
We can also derive the following un-DAG-like consequences, of course:

She2:
    <case person> = nominative
    <person number> = third
    <referent referent referent> = < NP referent >.
But these nonsensical theorems will be of no concern to a DATR-invoking NLP system that is able to specify in advance which paths are of interest for DAG-construction and to ignore all the rest (in this connection, see the discussion of ``closure definitions'' in Andry et al. 1992, 259-261).

A more sophisticated approach uses DATR itself to construct a DAG description (in the notation of your choice) as a value. This approach is due to recent unpublished work by Jim Kilbury . He has shown that the same DATR theorems can have their values realised as conventional attribute-value matrix representations, Prolog terms, or expressions of a feature logic, simply by changing the fine detail of the transducer employed.

    IDEM:
        <> ==
        <$atom> == $atom <>.
    PATH:
        <> == '<' IDEM '>'.
    LHS_EQ:
        <> == PATH '='.
    LHS_EQ_RHS:
        <> == LHS_EQ "<>".
    PRONOUN1:
        <dag> == [ LHS_EQ_RHS:<case>
                   LHS_EQ_RHS:<person>
                   LHS_EQ_RHS:<number>
                   LHS_EQ:<referent> PATH:<'NP' referent>  ].
    She1:
        <> == PRONOUN1
        <case> == nominative
        <person> == third
        <number> == singular.
From this description, we can derive the following theorem:

    She1:
        <dag> = [ < case > = nominative
                  < person > = third
                  < number > = singular
                  < referent > = < NP referent > ].
The sequence of atoms on the right hand side of this equation is just a sequence of atoms as far as DATR is concerned. But a grammar or a parser that expects to see DAGs represented as they are here can interpret the DATR values as easily as it can the contents of a file. Indeed, it will be interpreting the contents of a file if DATR has been used to define a lexicon that has subsequently been compiled out, rather than being accessed directly by components of the NLP system (see Section 7, below). We are not, of course, claiming that textual representations will standardly provide the optimal interface between an implementation of DATR and the larger NLP system in which it is embedded (cf., e.g., Duda & Gebhardi 1994).

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Left: Representing ambiguity and alternation Up: DATR techniques Right: Implementations and modes of
Copyright © Roger Evans, Gerald Gazdar & Bill Keller, Tuesday 10 November 1998