Automatically Building a Lexicon from Raw Noisy Data in a Closed Domain

Natural language that people use in electronic communication is far from perfect, due to the narrow channel. This also applies to electronic negotiation. We analyze characteristics of the language data obtained from electronic negotiation. We introduce a novel procedure for extracting and building a lexicon from raw noisy data. The data belong to a closed domain, which allows us to perform domaindependent word-sense disambiguation. The procedure itself is domain-independent and should work with data from various text collections. We present the results of an application of our procedure to a text corpus collected by an electronic negotiation support system. http: / / interneg.org/