A SVM-Based Model For Chinese Functional Chunk Parsing


Functional chunks are defined as a series of non-overlapping, non-nested segments of text in a sentence, representing the implicit grammatical relations between the sentence-level predicates and their arguments. Its top-down scheme and complexity of internal constitutions bring in a new challenge for automatic parser. In this paper, a new parsing model is proposed to formulate the complete chunking problem as a series of boundary detection subtasks. Each of these subtasks is only in charge of detecting one type of the chunk boundaries. As each subtask could be modeled as a binary classification problem, a lot of machine learning techniques could be applied. In our experiments, we only focus on the subject-predicate (SP) and predicate-object (PO) boundary detection subtasks. By applying SVM algorithm to these subtasks, we have achieved the best F-Score of 76.56% and 82.26% respectively.