Lyrebird [TM]: Developing Spoken Dialog Systems Using Examples

An early release software product for the rapid development of spoken dialog systems SDS's), known as Lyrebird [TM] [1][2][3], will be demonstrated that makes use of grammatical inference to build natural language, mixed initiative, speech recognition applications. The demonstration will consist of the presenter developing a spoken dialog system using Lyrebird [TM], and will include a demonstration of some features that are still in the prototype phase

Inferring Attribute Grammars with Structured Data for Natural Language Processing

This paper presents a method for inferring reversible attribute grammars from tagged natural language sentences. Attribute grammars are a form of augmented context free grammar that assign "meaning" in the form of a data structure to a string in a context free language. The method presented in this paper has the ability to infer attribute grammars that can generate a wide range of useful data structures such as simple and structured types, lists, concatenated strings, and natural numbers. The method also presents two new forms of grammar generalisation; generalisation based upon identification of optional phrases and generalisation based upon lists. The method has been applied to and tested on the task of the rapid development of spoken dialog systems

The Omphalos Context-Free Grammar Learning

This paper describes the Omphalos Context-Free Grammar Learning Competition held as part of the International Colloquium on Grammatical Inference 2004. The competition was created in an e#ort to promote the development of new and better grammatical inference algorithms for context-free languages, to provide a forum for the comparison of di#erent grammatical inference algorithms and to gain insight into the current state-of-the-art of context-free grammatical inference algorithms

The Boisdale algorithm - an induction method for a subclass of unification grammar from positive data

Abstract. This paper introduces a new grammatical inference algorithm called the Boisdale algorithm. This algorithm can identify a class of contextfree unification grammar in the limit from positive data only. The Boisdale algorithm infers both the syntax and the semantics of the language, where the semantics of the language can be described using arbitrarily complex data structures represented as key value pairs. The Boisdale algorithm is an alignment based learning algorithm that executes in polynomial time with respect to the length of the training data and can infer a grammar when presented with any set of sentences tagged with any data structure. This paper includes a description of the algorithm, a description of a class of language that it can identify in the limit and some experimental results. 1

IDENTIFYING LANGUAGES IN THE LIMIT USING ALIGNMENT-BASED LEARNING

ii I hereby certify that the work embodied in this thesis is the result of original research and has not been submitted for a higher degree to any other University or Institution. (Signed) __________________________

Progressing the state-of-the-art in grammatical inference by competition

International audienceThis paper describes the Omphalos Context-Free Language Learning Competition held as part of the International Colloquium on Grammatical Inference 2004. After the success of the Abbadingo Competition on the better known task of learning regular languages, the competition was created in an effort to promote the development of new and better grammatical inference algorithms for context-free languages, to provide a forum for the comparison of different grammatical inference algorithms and to gain insight into the current state-of-the-art of context-free grammatical inference algorithms. This paper discusses design issues and decisions made when creating the competition, leading to the introduction of a new complexity measure developed to estimate the difficulty of learning a context-free grammar. It presents also the results of the competition and lessons learned

The Tenjinno machine translation competition

This paper describes the Tenjinno Machine Translation Competition held as part of the International Colloquium on Grammatical Inference 2006. The competition aimed to promote the development of new and better practical grammatical inference algorithms used in machine translation. Tenjinno focuses on formal models used in machine translation. We discuss design issues and decisions made when creating the competition. For the purpose of setting the competition tasks, a measure of the complexity of learning a transducer was developed. This measure has enabled us to compare the competition tasks to other published results, and it can be seen that the problems solved in the competition were of a greater complexity and were solved with lower word error rates than other published results. In addition the complexity measures and benchmark problems can be used to track the progress of the state-of-the-art into the future.13 page(s

Progressing the state-of-the-art in grammatical inference by competition

This paper describes the Omphalos Context-Free Language Learning Competition held as part of the International Colloquium on Grammatical Inference 2004. After the success of the Abbadingo Competition on the better known task of learning regular languages, the competition was created in an effort to promote the development of new and better grammatical inference algorithms for context-free languages, to provide a forum for the comparison of different grammatical inference algorithms and to gain insight into the current state-of-the-art of context-free grammatical inference algorithms. This paper discusses design issues and decisions made when creating the competition, leading to the introduction of a new complexity measure developed to estimate the difficulty of learning a context-free grammar. It presents also the results of the competition and lessons learned.23 page(s

The Omphalos Context-Free Grammar Learning Competition

12 page(s