Mining Chinese comparative sentences by semantic role labeling

This paper studies the problem of mining Chinese comparative sentences in text documents by using semantic role labeling (SRL). The comparative opinion can be divided into six semantic roles: holder, entity 1, comparative predicates, entity 2, attributes and sentiments. These six opinion elements were recognized and labeled by using SRL. A corpus of Chinese comparative sentences was manually labeled at first. Then a conditional random fields (CRFs) model was trained by learn from the corpus. Finally new comparative sentences were labeled by using this CRFs model, and comparative relations were extracted afterward.