2012 | OriginalPaper | Buchkapitel
Towards Domain Independent Why Text Segment Classification Based on Bag of Function Words
verfasst von : Katsuyuki Tanaka, Tetsuya Takiguchi, Yasuo Ariki
Erschienen in: AI 2012: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Increased attention has been focused on question answering (QA) technology as next generation search since it improves the usability of information acquisition from web. However, not much research has been conducted on “non-factoid-QA”, especially on
Why Question Answering
(
Why-QA
). In this paper, we introduce a machine learning approach to automatically construct a classifier with function words as features to perform
Why Text Segments Classification
(
WTS
classification) by using SVM. It is a process of detecting text segments describing
“reasons-causes”
and is a subtask of
Why-QA
mainly related to an answer extraction part. We argue that function words are a strong discriminator for
WTS
classification. Furthermore, since function words appear in almost all text segments regardless of the domain of the topic, it also enables construction of a domain independent classifier. The experimental results showed significant improvement over state-of-the-art results in terms of accuracy of
WTS
classification as well as domain independent capability.