Classification of Relative Clauses Using Easily Obtainable Features
Mi-Young Kim · International Journal of Computer Processing Of Languages · 2006
The detection of a gap in relative clauses is essential in syntactic and semantic analysis of natural language processing. However, it is difficult to recognize whether a relative clause has a gap or it is gapless. Previous work related to relative clauses has been focusing mostly on theoretic linguistics without practically automatic classification, or classifying relative clauses using deep-level knowledge only available for a specific language. So, this paper proposes automatic classification method of a relative clause — whether it has a gap or gapless —, using easily obtainable features from any language. Features are extracted from the lexical forms and POS-tags in a relative clause, its headnoun, and contexts around a relative clause. Based on Support Vector Machines learning algorithm, our proposed method outperformed the baseline system by 25.11 percent. We also analyze the contribution rate of each simple feature to the classification, and the effect of contexts around a relative clause on the classification performance.