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Relation extraction is a challenging task in biomedical text mining due to the complex of sentences in the biomedical literature. In this paper, we address multi-class relationship extraction problem from biomedical literature using Maximum Entropy model with simple word features. The proposed method is applied to extract the protein-protein interactions. Experiments show the method achieves an accuracy...
Relation extraction is the detection and classification of interactions between named entities. Recently, a lot of studies have focused on the relation extraction from biomedical literatures. Although various approaches have been applied on this area, most methods are either pre-requires biomedical lexicons or parsing templates which are not suitable for complexities of biomedical literatures. In...
We present a ME (Maximum Entropy) model for Semantic Chunk Annotation in a Chinese Question and Answer (Q&A) system. The model was derived from a corpus of real world questions, which are collected from some discussion groups on the Internet. The questions are supposed to be answered by other people, so the questions are very complex. The semantic chunks were introduced. Feature for the model...
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