Automatic identification of non-anaphoric anaphora in spoken dialog

• Published in: 2008 International Conference on Natural Language Processing and Knowledge Engineering pp. 1 - 6
• Main Authors: Zhongchao Fei, Xuanjing Huang, Fuliang Weng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2008
• Subjects: anaphora resolution; Classification tree analysis; Computer science; Decision trees; Feature extraction; Machine learning algorithms; non-anaphoric anaphora identification; Pattern matching; Spoken dialog; Statistics; Support vector machine classification; Support vector machines
• ISBN: 1424445159; 9781424427796; 1424427797; 9781424445158
• DOI: 10.1109/NLPKE.2008.4906761

Identification of non-anaphoric anaphora is an important step towards a full anaphora resolution. In this paper, we present an automatic identification approach for this task. In our work, some novel features are proposed, which are based on dependency grammars, surrounding words and their POS tags. All the features are automatically extracted using a part-of-speech (POS) tagger and a dependency parser. Our experiments are on a commonly available dialogue corpus, Trains-93. Several machine learning algorithms are used in the experiments, including CME, CRF and SVM. Results show that compared to the approaches used in the previous work, our algorithm is simpler and achieves a higher accuracy.