Machine Reading with Background Knowledge

TL;DR

This paper introduces background knowledge integration into machine reading, improving understanding and relation extraction, with state-of-the-art results on ambiguity resolution and compound noun analysis.

Contribution

It presents two novel methods leveraging background knowledge to enhance language understanding in machine reading systems.

Findings

State-of-the-art results on prepositional phrase attachment

Accurate extraction of relationships from compound nouns

Significant performance improvements over baseline methods

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.