A hybrid approach to automatic derivation of class-based selectional preferences is proposed. A lexicon of selectional preferences can assist in handling several forms of ambiguity, a major problem for MT. The approach combines knowledge-rich parsing and lexicons, with statistics and corpus data. We illustrate the use of a selectional preference lexicon for anaphora resolution.

Translations produced by an MT system can automatically be assigned a number that reflects the MT system’s confidence in their quality. We describe the design of such a confidence index, with focus on the contribution of source analysis, which plays a crucial role in many MT systems, including ours. Various problematic areas of source analysis are identified, and their impact on the overall confidence index is given. We will describe two methods of training the confidence index, one by hand-tuning of the heuristics, the other by linear regression analysis.

EasyEnglish is an authoring tool which is part of IBM’s internal SGML editing environment, Information Development Workbench. EasyEnglish is used as a preprocessing step for machine-translating IBM manuals. Although Easy English does some traditional grammar checking, its focus is on problems of structural ambiguity. Such problems include ambiguous attachment of participles, ambiguous scope in coordination, and ambiguous attachment of the agent phrase for double passives. Since we deal with truly ambiguous constructions, the system has no way of deciding on the desired interpretation; the system provides the user with a choice of rewriting suggestions, each forcing an unambiguous attachment. This paper describes the techniques for identifying structural ambiguities and generating unambiguous rewriting suggestions.

We present a newly designed transformational system for the MT system LMT, consisting of a transformational formalism, LMT-TL, and an algorithm for applying transformations written in this formalism. LMT-TL is both expressive and simple because of the systematic use of a powerful pattern matching mechanism that focuses on dependency trees. LMT-TL is a language in its own right, with no “escapes” to underlying programming languages. We first provide an overview of the complete LMT translation process (all newly redesigned), and then give a self-contained description of LMT-TL, with examples.