Identifying non-referential it: a machine learning approach incorporating linguistically motivated patterns

Authors:
Adriane Boyd;Whitney Gegg-Harrison;Donna Byron
Affiliations:
The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH
Venue:
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Year:
2005

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Abstract

In this paper, we present a machine learning system for identifying non-referential it. Types of non-referential it are examined to determine relevant linguistic patterns. The patterns are incorporated as features in a machine learning system which performs a binary classification of it as referential or non-referential in a POS-tagged corpus. The selection of relevant, generalized patterns leads to a significant improvement in performance.