Using sliding-window rule application and extraction filtering techniques, we propose a framework for extracting semantic frames from Thai textual phrases with unknown boundaries based on patterns of triggering terms. A supervised rule learning algorithm is used for constructing multi-slot extraction rules from hand-tagged training phrases. A filtering module is introduced for predicting rule application across phrase boundaries based on instantiation features of rule internal wildcards. The framework is applied to text documents in three domains with different target-phrase density and average lengths. The experimental results show that the filtering module improves precision and preserves high recall satisfactorily, yielding extraction performance comparable to frame extraction with manually identified phrase boundaries. © Springer-Verlag Berlin Heidelberg 2009.