Senseval automatic labeling of semantic roles using Maximum Entropy models

SENSEVAL Automatic Labeling of Semantic Roles using Maximum Entropy Models Namhee Kwon Information Science Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292 nkwon@isi.edu Michael Fleischman Messachusetts Institute of Technology 77 Massachusetts Ave Cambridge, MA 02139 mbf@mit.edu Eduard Hovy Information Science Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292 hovy@isi.edu Abstract As a task in SensEval-3, Automatic Labeling of Semantic Roles is to identify frame elements within a sentence and tag them with appropriate semantic roles given a sentence, a target word and its frame. We apply Maximum Entropy classification with feature sets of syntactic patterns from parse trees and officially attain 80.2% precision and 65.4% recall. When the frame element boundaries are given, the system performs 86.7% precision and 85.8% recall. 1 Introduction The Automatic Labeling of Semantic Roles track in SensEval-3 focuses on identifying frame elements in sentences and tagging them with their appropriate semantic roles based on FrameNet1. For this task, we extend our previous work (Fleischman et el., 2003) by adding a sentence segmentation step and by adopting a few additional feature vectors for Maximum Entropy Model. Following the task definition, we assume the frame and the lexical unit of target word are known although we have assumed only the target word is known in the previous work. 2 Model We separate the problem of FrameNet tagging into three subsequent processes: 1) sentence segmentation 2) frame element identification, and 3) semantic role tagging. We assume the frame element (FE) boundaries match the parse constituents, so we segment a sentence based on parse constituents. We consider steps 2) and 3) as classification problems. In frame element identification, we use a binary classifier to determine if each parse constituent is a FE or not, while, in semantic role tagging, we classify each 1 http://www.icsi.berkeley.edu/~framenet identified FE into its appropriate semantic role.2 Figure 1 shows the sequence of steps. He fastened the panel from an old radio to the headboard with sticky tape and tied the driving wheel to Pete 's cardboard box with string (He) (fastened the panel from an old radio to the headboard with sticky tape) (and) (tied) (the driving wheel) (to Pete 's cardboard box) (with string) (He) (the driving wheel) (to Pete 's cardboard box) (with string) Agent Item Goal Connector Input sentence 1) Sentence Segmentation: choose the highest constituents while separating target word 2) Frame Element Identification: apply ME classification to classify each segment into classes of FE (frame element), T (target), NO (none) then extract identified frame elements 3) Semantic Role Tagging: apply ME classification to classify each FE Into classes of various semantic roles Output role: Agent (He), Item (the driving wheel), Goal (to Pete’s cardboard box), Connector (with string) Fig. 1. The sequence of steps on a sample sentence having a target word “tied”. We train the ME models using the GIS algorithm (Darroch and Ratcliff, 1972) as implemented in the YASMET ME package (Och, 2002). We use the YASMET MEtagger …

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