Lexical choice for complex noun phrases: Structure, modifiers, and determiners

This paper presents a lexical choice component for complex noun phrases. We first explain why lexical choice for NPs deserves special attention within the standard pipeline architecture for a generator. The task of the lexical chooser for NPs is more complex than for clauses because the syntax of NPs is less understood than for clauses, and therefore, syntactic realization components, while they accept a predicate-argument structure as input for clauses, require a purely syntactic tree as input for NPs. The task of mapping conceptual relations to different syntactic modifiers is therefore left to the lexical chooser for NPs. The paper focuses on the syntagmatic aspect of lexical choice, identifying a process called “NP planning”. It focuses on a set of communicative goals that NPs can satisfy and specifies an interface between the different components of the generator and the lexical chooser. The technique presented for NP planning encapsulates a rich lexical knowledge and allows for the generation of a wide variety of syntactic constructions. It also allows for a large paraphrasing power because it dynamically maps conceptual information to various syntactic slots

Extended Functional Unification ProGrammars

Functional Unification Grammars (PUGs) are popular for natural language applications because the formalism uses very few primitives and is uniform and expressive. In our work on text generation, we have found that it also has annoying limitations: it is not adapted to the expression of simple yet very common taxonomic relations and it does not allow easy manipulation of complex data-structures like lists or sets. We present in this paper a set of extensions that keep the desirable properties of the formalism but make it more flexible and easier to use. We first introduce the notion of typed features and typed constituents. Types define a structure over the set of primitive symbols used by the formalism. We then introduce extended unification: specialized unification methods can be defined for user-defined data-types. This extends the power of the system to handle complex data-structures efficiently. Taking advantage of a structured set of primitives and of specialized unification methods, the resulting formalism is more flexible, easier to use and produces better documented grammars than traditional functional unification. It can therefore be used to address deeper levels of text generation than was possible before

A Procedure for the Selection of Connectives: How Deep Is the Surface?

We present an implemented procedure to select an appropriate connective to link two propositions. Each connective is defined as a set of constraints between features of the propositions it connects. Our focus has been to identify pragmatic features that can be produced by a deep generator to provide a simple representation of connectives. Using these features, we can account for a variety of connective usages. We describe how a surface generator can produce complex sentences when given these features in input. The selection procedure is implemented as part of a large functional unification grammar

Generating Connectives

We present an implemented procedure to select an appropriate connective to link two propositions, which is part of a large text generation system. Each connective is defined as a set of constraints between features of fire propositions it connects. Our focus has been to identify pragmatic features that can be produced by a deep generator to provide a simple representation of connectives. Using these features, we can account for a variety of connective usages, and we can distinguish between similar connectives. We describe how a surface generator can produce complex sentences when given these features in input. The selection procedure is implemented as part of a large functional unification grammar

But What Do You Need To Produce a But?

We study the problem of appropriately generating connectives (e.g., 'but', 'because', 'since', 'however') in a discourse. We claim that connectives operate at the discourse level rather than the semantic level, and that they indicate pragmatic features of the units they connect. Therefore, in order to choose the appropriate connective, a surface generator must find in its input a set of pragmatic features that affect or are affected by the choice of a connective. We present such a set of features and show their role in a variety of examples of the connective 'but'

Comparison of Surface Language Generators: A Case Study in Choice of Connectives

Language generation systems have used a variety of grammatical formalisms for producing syntactic structure and yet, there has been little research evaluating the formalisms for the specifics of the generation task. In our work at Columbia we have primarily used a unification based formalism, a Functional Unification Grammar (FUG) [Kay 79] and have found it well suited for many of the generation tasks we have addressed. Over the course of the past 5 years we have also explored the use of various off-the-shelf parsing formalisms, including an Augmented Transition Network (ATN) [Woods 70]. a Bottom-Up Chan Parser (BUP) [Finin 84], and a Declarative Clause Grammar (DCG) [Pereira & Warren 80]. In this paper, we identify the characteristics of FDG that we find useful for generation and contrast these with characteristics of the parsing formalisms and with other formalisms that are typically used for generation