  Mining textual patterns in news, tweets, papers, and many other kinds of text
corpora has been an active theme in text mining and NLP research. Previous
studies adopt a dependency parsing-based pattern discovery approach. However,
the parsing results lose rich context around entities in the patterns, and the
process is costly for a corpus of large scale. In this study, we propose a
novel typed textual pattern structure, called meta pattern, which is extended
to a frequent, informative, and precise subsequence pattern in certain context.
We propose an efficient framework, called MetaPAD, which discovers meta
patterns from massive corpora with three techniques: (1) it develops a
context-aware segmentation method to carefully determine the boundaries of
patterns with a learnt pattern quality assessment function, which avoids costly
dependency parsing and generates high-quality patterns; (2) it identifies and
groups synonymous meta patterns from multiple facets---their types, contexts,
and extractions; and (3) it examines type distributions of entities in the
instances extracted by each group of patterns, and looks for appropriate type
levels to make discovered patterns precise. Experiments demonstrate that our
proposed framework discovers high-quality typed textual patterns efficiently
from different genres of massive corpora and facilitates information
extraction.
