Automatic acquisition of novel compounds is notoriously difficult because most novel compounds have relatively low frequency in a corpus. The current study proposes a new method to deal with the novel compound acquisition challenge. We model this task as a two-class classification problem in which a candidate compound is either classified as a compound or a non-compound. A machine learning method using SVM, incorporating two types of linguistically motivated features: semantic features and character features, is applied to identify rare but valid noun compounds. We explore two kinds of training data: one is virtual training data which is obtained by three statistical scores, i.e. co-occurrence frequency, mutual information and dependent ratio, from the frequent compounds; the other is real training data which is randomly selected from the infrequent compounds. We conduct comparative experiments, and the experimental results show that even with limited direct evidence in the corpus for the novel compounds, we can make full use of the typical frequent compounds to help in the discovery of the novel compounds.

We propose a set of heuristics for improving annotation quality of very large corpora efficiently. The Xinhua News portion of the Chinese Gigaword Corpus was tagged independently with both the Peking University ICL tagset and the Academia Sinica CKIP tagset. The corpus-based POS tags mapping will serve as the basis of the possible contrast in grammatical systems between PRC and Taiwan. And it can serve as the basic model for mapping between the CKIP and ICL tagging systems for any data.

This paper describes the progress of a machine translation system from Chinese to English. The system is based on a reusable platform of MT software components. It’s a rule-based system, and some statistical algorithms are used as heuristic functions in parsing as well. There are about 50,000 Chinese words and 400 global parsing rules in the system. The system got a good result in a public test of MT system in China in Mar. 1998. It is a research vehicle up to now.