Shallow Semantics with Shallow Syntax
نویسنده
چکیده
Assigning semantic roles to the constituents of a natural language sentence is an important first step in translating natural language into a logical form for further processing. I present a statistical classifier which can perform this task using minimal syntactic cues. I use the syntactic and the semantic head of each constituent as the only features and present simple rules for extracting these heads. I also show how a hierarchical tree of semantic roles can be exploited to better estimate the probabilities in the model. On the FrameNet 1.3 corpus, my system can label pre-segmented constituents with an accuracy of 79.6%.
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