BBN and FinCEN participated jointly in the Spanish language task for MET. BBN also participated in Chinese. We also fielded two approaches. The first approach is pattern based and has an architecture as shown in Figure 1. This approach was applied to both Chinese and Spanish. The algorithms (rectangles in the Figure) were used in the two languages; the only component difference was the New Mexico State University segmenter, used to find the word boundaries in Chinese. The components common to both languages are the message reader, which dealt with the input format and SGML conventions via a declarative format description; the part-of-speech tagger (BBN POST); a lexical pattern matcher driven by knowledge bases of patterns and lexicons specific to each language; and the SGML annotation generator. While not shown in Figure 1, an alias prediction algorithm was shared by both languages, using patterns unique to each language.