Identifying Opinion Holders for Question Answering in Opinion Texts Soo-Min Kim and Eduard Hovy Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292-6695 {skim, hovy}@isi.edu Advisor: Hsin-Hsi Chen Speaker: Yong-Sheng Lo Date: 2007/08/16 AAAI - 2005
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Identifying Opinion Holders for Question Answering in Opinion Texts Soo-Min Kim and Eduard Hovy Information Sciences Institute University of Southern California.
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Identifying Opinion Holders for Question
Answering in Opinion Texts
Soo-Min Kim and Eduard HovyInformation Sciences Institute
University of Southern California4676 Admiralty Way
Marina del Rey, CA 90292-6695{skim, hovy}@isi.edu
Advisor: Hsin-Hsi ChenSpeaker: Yong-Sheng Lo
Date: 2007/08/16
AAAI - 2005
Introduction 1/2 Question answering in opinion texts
“Who strongly believes in Y” A system to recognize the holder of opinion Y
Application Stock market predictors
Earlier work (Kim and Hovy,2004) Focus on identifying opinion expressions within text 現在進一步要找出 opinion holder
2. There is more than one opinion in a sentence “In relation to Bush’s axis of evil remarks, the German
Foreign Minister also said, Allies are not satellites, and the French Foreign Minister caustically criticized that the United States’ unilateral, simplistic worldview poses a new threat to the world”.
本文提的解法 Automatic method for identifying opinion holders (OH)
1. Identify all possible opinion holder entities in a sentence 使用現有工具找出句子中的Name entities 和 Noun phrases
2. Apply the Maximum Entropy (ME) ranking algorithm to select the most probable entity
System architecture
Holder candidate set
Named entities (NE) Using BBN’s named entity tag
ger IdentiFinder Noun phrases (NP)
Using Charniak’s parser For example
Maximum Entropy ranking algorithm
A machine learning approach Maximum Entropy modeling
Classification Select many candidates as answers as long as they are
marked as true and does not select any candidate if every one is marked as false
Poor performance Ranking
Select the most probable candidate as an answer To maximize a given conditional probability distribution
Training data MPQA corpus (Wiebe et al., 2003)
535 documents (10657 sentences) 以下是標記者的標記例子:
只選意見強度 (Strength) 為 high or extreme 的句子
Opinion
Holder
Training 流程
Feature selection for ME1. Full parsing features (f2,f3,f4,f6)2. Partial parsing features (f7,f8,f9)3. Others (f1,f5)
Full parsing features 1/5 Using charniak’s parser For example:
China’s official Xinhua news agency <H> Form MPQA
accusing <E> From Earlier work (Kim and Hovy,2004)
Full parsing features 2/5
Full parsing features 3/5 To express tree structure for ME training
“<H> NP S VP S S VP VBG <E>” Data sparseness problem
Full parsing features 4/5 Solution: 分成三條 path(f2,f3,f4)