Paper Info
Title
Machine Reading with Background Knowledge.
Abstract
Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Understanding language often constitutes fitting new information into a previously acquired view of the world. However, many machine reading systems rely on the text alone to infer its meaning. In this paper, we pursue a different approach; machine reading methods that make use of background knowledge to facilitate language understanding. To this end, we have developed two methods: The first method addresses prepositional phrase attachment ambiguity. It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data. This approach yields state-of-the-art results on two datasets against strong baselines; The second method extracts relationships from compound nouns. Our knowledge-aware method for compound noun analysis accurately extracts relationships and significantly outperforms a baseline that does not make use of background knowledge.
Year
Venue
Field
2016
arXiv: Artificial Intelligence
Intelligent decision support system,Computer science,Noun,Phrase,Natural language,Natural language processing,Artificial intelligence,Mobile phone,Ambiguity,Robotics,Machine learning,Applications of artificial intelligence
DocType
Volume
Citations 
Journal
abs/1612.05348
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ndapandula Nakashole139419.48
Tom M. Mitchell271601946.42