1,730 research outputs found
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Efficient methods for storing and querying are critical for scaling
high-order n-gram language models to large corpora. We propose a language model
based on compressed suffix trees, a representation that is highly compact and
can be easily held in memory, while supporting queries needed in computing
language model probabilities on-the-fly. We present several optimisations which
improve query runtimes up to 2500x, despite only incurring a modest increase in
construction time and memory usage. For large corpora and high Markov orders,
our method is highly competitive with the state-of-the-art KenLM package. It
imposes much lower memory requirements, often by orders of magnitude, and has
runtimes that are either similar (for training) or comparable (for querying).Comment: 14 pages in Transactions of the Association for Computational
Linguistics (TACL) 201
Handling Massive N-Gram Datasets Efficiently
This paper deals with the two fundamental problems concerning the handling of
large n-gram language models: indexing, that is compressing the n-gram strings
and associated satellite data without compromising their retrieval speed; and
estimation, that is computing the probability distribution of the strings from
a large textual source. Regarding the problem of indexing, we describe
compressed, exact and lossless data structures that achieve, at the same time,
high space reductions and no time degradation with respect to state-of-the-art
solutions and related software packages. In particular, we present a compressed
trie data structure in which each word following a context of fixed length k,
i.e., its preceding k words, is encoded as an integer whose value is
proportional to the number of words that follow such context. Since the number
of words following a given context is typically very small in natural
languages, we lower the space of representation to compression levels that were
never achieved before. Despite the significant savings in space, our technique
introduces a negligible penalty at query time. Regarding the problem of
estimation, we present a novel algorithm for estimating modified Kneser-Ney
language models, that have emerged as the de-facto choice for language modeling
in both academia and industry, thanks to their relatively low perplexity
performance. Estimating such models from large textual sources poses the
challenge of devising algorithms that make a parsimonious use of the disk. The
state-of-the-art algorithm uses three sorting steps in external memory: we show
an improved construction that requires only one sorting step thanks to
exploiting the properties of the extracted n-gram strings. With an extensive
experimental analysis performed on billions of n-grams, we show an average
improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February
2019, Article No: 2
Substring filtering for low-cost linked data interfaces
Recently, Triple Pattern Fragments (TPFS) were introduced as a low-cost server-side interface when high numbers of clients need to evaluate SPARQL queries. Scalability is achieved by moving part of the query execution to the client, at the cost of elevated query times. Since the TPFS interface purposely does not support complex constructs such as SPARQL filters, queries that use them need to be executed mostly on the client, resulting in long execution times. We therefore investigated the impact of adding a literal substring matching feature to the TPFS interface, with the goal of improving query performance while maintaining low server cost. In this paper, we discuss the client/server setup and compare the performance of SPARQL queries on multiple implementations, including Elastic Search and case-insensitive FM-index. Our evaluations indicate that these improvements allow for faster query execution without significantly increasing the load on the server. Offering the substring feature on TPF servers allows users to obtain faster responses for filter-based SPARQL queries. Furthermore, substring matching can be used to support other filters such as complete regular expressions or range queries
Byte-Aligned Pattern Matching in Encoded Genomic Sequences
In this article, we propose a novel pattern matching algorithm, called BAPM, that performs searching in the encoded genomic sequences. The algorithm works at the level of single bytes and it achieves sublinear performance on average. The preprocessing phase of the algorithm is linear with respect to the size of the searched pattern m. A simple O(m)-space data structure is used to store all factors (with a defined length) of the searched pattern. These factors are later searched during the searching phase which ensures sublinear time on average. Our algorithm significantly overcomes the state-of-the-art pattern matching algorithms in the locate time on middle and long patterns. Furthermore, it is able to cooperate very easily with the block q-gram inverted index. The block q-gram inverted index together with our pattern matching algorithm achieve superior results in terms of locate time to the current index data structures for less frequent patterns. We present experimental results using real genomic data. These results prove efficiency of our algorithm
A new method for indexing genomes using on-disk suffix trees
We propose a new method to build persistent suffix trees for indexing the genomic data. Our algorithm DiGeST (Disk-Based Genomic Suffix Tree) improves significantly over previous work in reducing the random access to the in-put string and performing only two passes over disk data. DiGeST is based on the two-phase multi-way merge sort paradigm using a concise binary representation of the DNA alphabet. Furthermore, our method scales to larger genomic data than managed before
Fast Label Extraction in the CDAWG
The compact directed acyclic word graph (CDAWG) of a string of length
takes space proportional just to the number of right extensions of the
maximal repeats of , and it is thus an appealing index for highly repetitive
datasets, like collections of genomes from similar species, in which grows
significantly more slowly than . We reduce from to
the time needed to count the number of occurrences of a pattern of
length , using an existing data structure that takes an amount of space
proportional to the size of the CDAWG. This implies a reduction from
to in the time needed to
locate all the occurrences of the pattern. We also reduce from
to the time needed to read the characters of the
label of an edge of the suffix tree of , and we reduce from
to the time needed to compute the matching
statistics between a query of length and , using an existing
representation of the suffix tree based on the CDAWG. All such improvements
derive from extracting the label of a vertex or of an arc of the CDAWG using a
straight-line program induced by the reversed CDAWG.Comment: 16 pages, 1 figure. In proceedings of the 24th International
Symposium on String Processing and Information Retrieval (SPIRE 2017). arXiv
admin note: text overlap with arXiv:1705.0864
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