33,863 research outputs found
Adaptive text mining: Inferring structure from sequences
Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively
Universal Indexes for Highly Repetitive Document Collections
Indexing highly repetitive collections has become a relevant problem with the
emergence of large repositories of versioned documents, among other
applications. These collections may reach huge sizes, but are formed mostly of
documents that are near-copies of others. Traditional techniques for indexing
these collections fail to properly exploit their regularities in order to
reduce space.
We introduce new techniques for compressing inverted indexes that exploit
this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar
compression of the differential inverted lists, instead of the usual practice
of gap-encoding them. We show that, in this highly repetitive setting, our
compression methods significantly reduce the space obtained with classical
techniques, at the price of moderate slowdowns. Moreover, our best methods are
universal, that is, they do not need to know the versioning structure of the
collection, nor that a clear versioning structure even exists.
We also introduce compressed self-indexes in the comparison. These are
designed for general strings (not only natural language texts) and represent
the text collection plus the index structure (not an inverted index) in
integrated form. We show that these techniques can compress much further, using
a small fraction of the space required by our new inverted indexes. Yet, they
are orders of magnitude slower.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
Using compression to identify acronyms in text
Text mining is about looking for patterns in natural language text, and may
be defined as the process of analyzing text to extract information from it for
particular purposes. In previous work, we claimed that compression is a key
technology for text mining, and backed this up with a study that showed how
particular kinds of lexical tokens---names, dates, locations, etc.---can be
identified and located in running text, using compression models to provide the
leverage necessary to distinguish different token types (Witten et al., 1999)Comment: 10 pages. A short form published in DCC200
Re-Pair Compression of Inverted Lists
Compression of inverted lists with methods that support fast intersection
operations is an active research topic. Most compression schemes rely on
encoding differences between consecutive positions with techniques that favor
small numbers. In this paper we explore a completely different alternative: We
use Re-Pair compression of those differences. While Re-Pair by itself offers
fast decompression at arbitrary positions in main and secondary memory, we
introduce variants that in addition speed up the operations required for
inverted list intersection. We compare the resulting data structures with
several recent proposals under various list intersection algorithms, to
conclude that our Re-Pair variants offer an interesting time/space tradeoff for
this problem, yet further improvements are required for it to improve upon the
state of the art
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