A Maximum Entropy Model Based Answer Extraction for Chinese Question Answering

We regard answer extraction of Question Answering (QA) system as a classification problem, classifying answer candidate sentences into positive or negative. To confirm the feasibility of this new approach, we first extract features concerning question sentences and answer words from question answer pairs (QA pair), then we conduct experiments based on these features, using Maximum Entropy Model (MEM) as a Machine Learning (ML) technique. The

• Type: Conference
• Year: 2006
• Where: FSKD
• Authors: Ang Sun, Minghu Jiang, Yanjun Ma
• Keywords: Answer Candidate, Answer Extraction, FSKD 2006, Fuzzy Logic, Question Answer Pairs