Abstract:  The paper is aimed to extract affiliation relations between entities in the Thai research domain.An approach of the affiliation relations extraction between entities in sentences of Thai language based on the maximum entropy model is proposed.As for the deficience of corpus in the relation extraction process between entities in the sentences of Thai language，by making full use of the parallel sentence pairs of Chinese－Thai bilingual as an intermediate bridge，the comparative mature research findings in Chinese research domian，which including word segmentation，POS tagging，entity recognition and so on，will be mapped to the sentences of Thai language which corresponding to the sentences of Chinese with the help of Chinese－Thai bilingual dictionary.Then we operate several data processing procedures of Thai sentences and conduct appropriate manual amendments，as well as labeling the entity relation samples manually.Consequently，the training corpus infrastructure of entity relations extraction in Thai language is built.On the basis of the corpus，we treat the entity relations extraction problem as a classification task.Given several particular characteristics of the Thai language itself，certain features templates in context of samples are extracted to train the maximum entropy model to be a useful classifier.Thus the model is able to recognize the class of triple tuples of candidate entity affiliation realtions to verify the efficiency and precision of the classifier in order to accomplish the task of the affiliation relations extraction between entities.The experiments show that the approach put forward in the paper can enable the F－measure to improve 8% approximately compared with the existing methods.