Robust Grammatical Analysis for Spoken Dialogue Systems

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.Comment: Accepted for JNL

Instance-based natural language generation

In recent years, ranking approaches to Natural Language Generation have become increasingly popular. They abandon the idea of generation as a deterministic decision¬ making process in favour of approaches that combine overgeneration with ranking at some stage in processing.In this thesis, we investigate the use of instance-based ranking methods for surface realization in Natural Language Generation. Our approach to instance-based Natural Language Generation employs two basic components: a rule system that generates a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to examples taken from a training corpus. The instance-based ranker uses information retrieval methods to rank output candidates.Our approach is corpus-based in that it uses a treebank (a subset of the Penn Treebank II containing management succession texts) in combination with manual semantic markup to automatically produce a generation grammar. Furthermore, the corpus is also used by the instance-based ranker. The semantic annotation of a test portion of the compiled subcorpus serves as input to the generator.In this thesis, we develop an efficient search technique for identifying the optimal candidate based on the A*-algorithm, detail the annotation scheme and grammar con¬ struction algorithm and show how a Rete-based production system can be used for efficient candidate generation. Furthermore, we examine the output of the generator and discuss issues like input coverage (completeness), fluency and faithfulness that are relevant to surface generation in general

Robust Processing of Natural Language

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro, pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture Notes in Computer Science, Springer 199

Word order and discontinuities in dependency grammar

Natural languages are always difficult to parse. Two phenomena that constantly pose problems for different formalisms are word order - what part of a sentence has to be placed where - and discontinuities - words that belong together but are not placed into the same phrase. Dependency grammar, a linguistic formalism based on binary relations between words, is very adequate for handling both problems. A parser for dependency grammar together with its grammar writing formalism is described in this paper. Word order and discontinuities in Hungarian are handled based on this formalism

Concurrent Lexicalized Dependency Parsing: The ParseTalk Model

A grammar model for concurrent, object-oriented natural language parsing is introduced. Complete lexical distribution of grammatical knowledge is achieved building upon the head-oriented notions of valency and dependency, while inheritance mechanisms are used to capture lexical generalizations. The underlying concurrent computation model relies upon the actor paradigm. We consider message passing protocols for establishing dependency relations and ambiguity handling.Comment: 90kB, 7pages Postscrip

Parsing natural language

People have long been intrigued by the possibility of using a computer to understand natural language. Most researchers attempting to solve this problem have begun their efforts by trying to have the computer recognize the underlying syntactic form (the parse tree) of the sentence. This thesis presents an overview of the history of syntactic parsing of natural language, and it compares the major methods that have been used. Linguistically, two recent grammars are described: transformational grammar and systemic grammar. Computationally, three parsing strategies are described and compared: top-down parsing, bottom-up parsing, and a combination of both of these methods. Several important natural language systems are described, including Woods\u27 LUNAR program, Winograd\u27s SHRDLU, and Marcus\u27 PARSIFAL

Syntax, morphology, and phonology in text-to-speech systems

The paper is concerned with the integration of linguistic information in text-to-speech systems. Research in synthesis proper is at a stage where the need for systematic integration of comprehensive linguistic information in such systems is making itself felt more than ever. A surface structure parsing system is presented whose main virtue is that it permits linguists to express syntactic as well as lexical and morphological regularities and irregularities of a language in a simple and easy-to-learn formalism. Most aspects of the system are seen in the light of Danish and - sporadically - English and Finnish surface structure