This paper presents an approach to automatically build a semantic perceptron net (SPN) for topic spotting. It uses context at the lower layer to select the exact meaning of key words, and employs a combination of context, co-occurrence statistics and thesaurus to group the distributed but semantically related words within a topic to form basic semantic nodes. The semantic nodes are then used to infer the topic within an input document. Experiments on Reuters 21578 data set demonstrate that SPN is able to capture the semantics of topics, and it performs well on topic spotting task.
['Jimin Liu', 'Tat-Seng Chua']
Building Semantic Perceptron Net for Topic Spotting