Benoit Lavoie

A Fast and Portable Realizer for Text Generation Systems

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Conceptual modeling through linguistic analysis using LIDA

Despite the advantages that object technology can provide to the software development community and its customers, the fundamental problems associated with identifying objects, their attributes, and methods remain: it is a largely manual process driven by heuristics that analysts acquire through experience. While a number of methods exist for requirements development and specification, very few tools exist to assist analysts in making the transition from textual descriptions to other notations for object-oriented analysis and other conceptual models. We describe a methodology and a prototype tool. Linguistic Assistant for Domain Analysis (LIDA), which provide linguistic assistance in the model development process. We first present our methodology to conceptual modeling through linguistic analysis. We give an overview of LIDAs functionality and present its technical design and the functionality of its components. We also provide a comparison of LIDAs functionality with that of other research prototypes. Finally, we present an example of how LIDA is used in a conceptual modeling task.

Handling Stuctural Divergences and Recovering Dropped Arguments in a Korean/English Machine Translation System

This paper describes an approach for handling structural divergences and recovering dropped arguments in an implemented Korean to English machine translation system. The approach relies on canonical predicate-argument structures (or dependency structures), which provide a suitable pivot representation for the handling of structural divergences and the recovery of dropped arguments. It can also be converted to and from the interface representations of many off-the-shelf parsers and generators.

Inducing lexico-structural transfer rules from parsed Bi-texts

This paper describes a novel approach to inducing lexico-structural transfer rules from parsed bi-texts using syntactic pattern matching, statistical co-occurrence and error-driven filtering. We present initial evaluation results and discuss future directions.

Learning domain-specific transfer rules: an experiment with Korean to English translation

We describe the design of an MT system that employs transfer rules induced from parsed bitexts and present evaluation results. The system learns lexico-structural transfer rules using syntactic pattern matching, statistical co-occurrence and error-driven filtering. In an experiment with domain-specific Korean to English translation, the approach yielded substantial improvements over three baseline systems.

A Framework for Customizable Generation of Hypertext Presentations

In this paper, we present a framework, PRESENTOR, for the development and customization of hypertext presentation generators. PRESENTOR offers intuitive and powerful declarative languages specifying the presentation at different levels: macro-planning, micro-planning, realization, and formatting. PRESSENTOR is implemented and is portable cross-platform and cross-domain. It has been used with success in several application domains including weather forecasting, object modeling, system description and requirements summarization.

Towards translingual information access using portable information extraction

We report on a small study undertaken to demonstrate the feasibility of combining portable information extraction with MT in order to support translingual information access. After describing the proposed systems usage scenario and system design, we describe our investigation of transferring information extraction techniques developed for English to Korean. We conclude with a brief discussion of related MT issues we plan to investigate in future work.