MatsLex- a Multilingual Lexical Database for Machine Translation

MatsLex represents a relational database which can be used to store multilingual lexical data in a central and coherent lexicon. Tools and interfaces have been implemented to maintain the database and to apply its contents to different multilingual applications. MatsLex has been developed to feed different modules of a machine translation system with appropriate data, monolingual as well as bilingual. The database gives the user full control of the lexicon. In the paper, features and interfaces of the database are discussed as well as the connection to the machine translation engine. 1. Overview MatsLex is a multilingual lexical database that has been developed in the MATS project at Uppsala University/Sweden (MATS, 2000; S˚agvall Hein et al., forthcoming; Weijnitz, forthcoming). The primary aim of the project is the scaling up of the transfer-based machine translation prototype MULTRA (Beskow, 1993; S˚agvall Hein, 1997) for one domain. For this purpose, lexical resources have been derived from corpora (Tiedemann, 1999) and stored in the MatsLex database. The database is designed to provide a flexible and coherent environment for storing and managing multilingual lexical data, and for linking them bilingually. The internal structure of the lexicon is based on a relational database model. The database can be queried and updated via transparent database views in web-based interfaces. MatsLex is the central store of all the lexical data available for the translation process, and from this ”runtime lexicons” such as bilingual link lexicons are compiled. For consistency, modifications are allowed in the central database only whereas runtime lexicons are strictly read-only. An overview of the MatsLex database and its connection to the machine translation system is sketched in figure 1. Figure 1: MatsLex and MULTRA. The structure of the database will be presented in section 2., database views and their functionality are presented in section 3.1.. The compilation of run-time lexicons as input for the machine translation system is then presented i