Summary of the paper
| Title | Automatic Acquisition of Chinese Novel Noun Compounds |
|---|---|
| Authors | Meng Wang, Chu-Ren Huang, Shiwen Yu and Weiwei Sun |
| Abstract | 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. |
| Topics | MultiWord Expressions & Collocations, Acquisition, Lexicon, lexical database |
Full paper |
Automatic Acquisition of Chinese Novel Noun Compounds |
Slides |
- |
| Bibtex | @InProceedings{WANG10.377, author = {Meng Wang and Chu-Ren Huang and Shiwen Yu and Weiwei Sun}, title = {Automatic Acquisition of Chinese Novel Noun Compounds}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |