A syntax-directed translator first parses the source-language input into a parsetree, and then recursively converts the tree into a string in the target-language. We model this conversion by an extended treeto-string transducer that have multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to rescore with other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for n-gram rescoring. Initial experimental results on English-to-Chinese translation are presented. 1

this paper, we introduce a modi cation of the last algorithm that can be trained with positive samples generated according to a probabilistic production scheme. The construction follows the same guidelines as the algorithm for string languages in Carrasco and Oncina (1998). A dierent approach (Sakakibara et al., 1994) generalizes the

We discuss the concept of robustness with respect to parsing or recognizing a contextfree language. Our approach is based on the notions of fuzzy language, (generalized) fuzzy context-free grammar, and parser/recognizer for fuzzy languages. As concrete examples we consider a robust version of Cocke–Younger–Kasami’s algorithm and a robust kind of recursive descent recognizer.

Abstract − Using fuzzy context-free grammars one can easily describe a finite number of ways to derive incorrect strings together with their degree of correctness. However, in general there is an infinite number of ways to perform a certain task wrongly. In this paper we introduce a generalization of fuzzy context-free grammars, the so-called fuzzy context-free K-grammars, to model the situation of making a finite choice out of an infinity of possible grammatical errors during each context-free derivation step. Under minor assumptions on the parameter K this model happens to be a very general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism. Our first result characterizes the generating capacity of these fuzzy context-free K-grammars. As consequences we obtain: (i) bounds on modeling grammatical errors within the framework of fuzzy context-free grammars, and (ii) the fact that the family of languages generated by fuzzy context-free K-grammars shares closure properties very similar to those of the family of ordinary context-free languages. The second part of the paper is devoted to a few algorithms to recognize fuzzy context-free languages: viz. a variant of a functional version of Cocke−Younger− Kasami’s algorithm and some recursive descent algorithms. These algorithms turn out to be robust in some very elementary sense and they can easily be extended to corresponding parsing algorithms. 1.

We consider the inference problem for finite transducers using different kinds of samples (positive and negative samples, positive samples only, and structural samples). Given pairs of input and output words, our task is to infer the finite transducer consistent with the given pairs. We show that this problem can be solved in certain special cases by using known results on the inference problem for linear languages. Keywords: formal languages, inductive inference, finite transducers, linear languages. 1 Introduction A finite transducer is a finite automaton which emits an output string during each move made. It defines a translation, i.e. a set of pairs of strings. In each pair (ff; fi), ff is a word over the input alphabet and fi is a word over the output alphabet. A classical work considering the use of translations on compilers is [1]. This note deals with the inductive inference properties of finite transducers and the translations realized by them. Given a set of pairs of input a...

Abstract — We investigate context-free grammars the rules of which can be used in a productive and in a reductive fashion, while the application of these rules is controlled by a regular language. We distinguish several modes of derivation for this kind of grammar. The resulting language families (properly) extend the family of context-free languages. We establish some closure properties of these language families and some grammatical transformations which yield a few normal forms for this type of grammar. Finally, we consider some special cases (viz. the context-free grammar is linear or left-linear), and generalizations, in particular, the use of arbitrary rather than regular control languages. KEY WORDS: controlled context-free grammar, production and reduction (i.e. reversed production), mode of derivation, normal form, closure

Abstract. Grammars written as Constraint Handling Rules (CHR) can be executed as efficient and robust bottom-up parsers that provide a straightforward, non-backtracking treatment of ambiguity. Abduction with integrity constraints as well as other dynamic hypothesis generation techniques fit naturally into such grammars and are exemplified for anaphora resolution, coordination and text interpretation. 1

Abstract − We provide a reformulation of Cocke−Younger−Kasami’s algorithm for recognizing context-free languages in which there are no references either to indices of table entries or to the length of the input string. Some top-down analogues of this functional approach are discussed as well.

Abstract. This paper describes a purely functional implementation of LR parsing. We formally derive our parsers in a series of steps starting from the inverse of printing. In contrast to traditional implementations of LR parsing, the resulting parsers are fully typed, stackless and table-free. The parsing functions pursue alternatives in parallel with each alternative represented by a continuation argument. The direct implementation presents many opportunities for optimization and initial measurements show excellent performance in comparison with conventional table-driven parsers. Keywords: continuations, derivation, LL- and LR parsing, efficiency. 1.

Motivated by aspects of robustness in parsing a context-free language, we study generalized fuzzy context-free grammars. These fuzzy context-free K-grammars provide a general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism. They model the situation of making a finite choice out of an infinity of possible grammatical errors during each context-free derivation step. Formally, a fuzzy context-free K-grammar is a fuzzy context-free grammar with a countable rather than a finite number of rules satisfying the following condition: for each symbol α, the set containing all right-hand sides of rules with left-hand side equal to α forms a fuzzy language that belongs to a given family K of fuzzy languages. We investigate the generating power of fuzzy context-free K-grammars, and we show that under minor assumptions on the parameter K, the family of languages generated by fuzzy context-free K-grammars possesses closure properties very similar to those of the family of ordinary context-free languages. Key words: formal language, fuzzy context-free grammar, grammatical error, algebraic closure property 1