The methodology developed within the FrameNet project is being used to compile resources in an increasing number of specialized fields of knowledge. The methodology along with the theoretical principles on which it is based, i.e. Frame Semantics, are especially appealing as they allow domain-specific resources to account for the conceptual background of specialized knowledge and to explain the linguistic properties of terms against this background. This paper presents a methodology for building a multilingual resource that accounts for terms of the environment. After listing some lexical and conceptual differences that need to be managed in such a resource, we explain how the FrameNet methodology is adapted for describing terms in different languages. We first applied our methodology to French and then extended it to English. Extensions to Spanish, Portuguese and Chinese were made more recently. Up to now, we have defined 190 frames: 112 frames are new; 38 are used as such; and 40 are slightly different (a different number of obligatory participants; a significant alternation, etc.) when compared to Berkeley FrameNet.

The objectives of the work described in this paper are: 1. To list the differences between a general language resource (namely FrameNet) and a domain-specific resource; 2. To devise solutions to merge their contents in order to increase the coverage of the general resource. Both resources are based on Frame Semantics (Fillmore 1985; Fillmore and Baker 2010) and this raises specific challenges since the theoretical framework and the methodology derived from it provide for both a lexical description and a conceptual representation. We propose a series of strategies that handle both lexical and conceptual (frame) differences and implemented them in the specialized resource. We also show that most differences can be handled in a straightforward manner. However, some more domain specific differences (such as frames defined exclusively for the specialized domain or relations between these frames) are likely to be much more difficult to take into account since some are domain-specific.

This paper proposes a method for discovering semantic frames (Fillmore, 1982, 1985; Fillmore et al., 2003) in specialized domains. It is assumed that frames are especially relevant for capturing the lexical structure in specialized domains and that they complement structures such as ontologies that appear better suited to represent specific relationships between entities. The method we devised is based on existing lexical entries recorded in a specialized database related to the field of the environment (erode, impact, melt, recycling, warming). The frames and the data encoded in FrameNet are used as a reference. Selected information was extracted automatically from the database on the environment (and, when possible, compared to FrameNet), and presented to a linguist who analyzed this information to discover potential frames. Several different frames were discovered with this method. About half of them correspond to frames already described in FrameNet; some new frames were also defined and part of these might be specific to the field of the environment.

We present a new version of a Graphical User Interface (GUI) called DiCoInfo Visuel, mainly based on a graph visualization device and used for exploring and assessing lexical data found in DiCoInfo, a specialized e-dictionary of computing and the Internet. This new GUI version takes advantage of the fundamental nature of the lexical network encoded in the dictionary: it uses logic based methods from logic programming to explore relations between entries and find pieces of relevant information that may be not accessible by direct searches. The result is a more realistic and useful data coverage shown to end users.

This paper describes the approach used for introducing CAT tools and MT systems into a course offered in translation curricula at the Université de Montréal (Canada). It focuses on the automation of the translation process and presents various strategies that have been developed to help students progressively acquire the knowledge necessary to understand and undertake the tasks involved in the automation of translation. We begin with very basic principles and techniques, and move towards complex processes of advanced CAT and revision tools, including ultimately MT systems. As we will see, teaching concepts related to MT serves both as a wrap-up for the subjects dealt with during the semester and a way to highlight the tasks involved in the transfer phase of translation.