Extracting knowledge from evaluative text

Capturing knowledge from free-form evaluative texts about an entity is a challenging task. New techniques of feature extraction, polarity determination and strength evaluation have been proposed. Feature extraction is particularly important to the task as it provides the underpinnings of the extracted knowledge. The work in this paper introduces an improved method for feature extraction that draws on an existing unsupervised method. By including user-specific prior knowledge of the evaluated entity, we turn the task of feature extraction into one of term similarity by mapping crude (learned) features into a user-defined taxonomy of the entity’s features. Results show promise both in terms of the accuracy of the mapping as well as the reduction in the semantic redundancy of crude features. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing—Text Analysis; I.7.5 [Document and Text Processing]: Document Capture—Document Analysis Genera...

• Type: Conference
• Year: 2005
• Where: KCAP
• Authors: Giuseppe Carenini, Raymond T. Ng, Ed Zwart
• Keywords: Feature Extraction, KCAP 2005, Keywords Feature Extraction, Term Similarity