1,926 research outputs found
A hybrid architecture for robust parsing of german
This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation
Statistical Function Tagging and Grammatical Relations of Myanmar Sentences
This paper describes a context free grammar (CFG) based grammatical relations
for Myanmar sentences which combine corpus-based function tagging system. Part
of the challenge of statistical function tagging for Myanmar sentences comes
from the fact that Myanmar has free-phrase-order and a complex morphological
system. Function tagging is a pre-processing step to show grammatical relations
of Myanmar sentences. In the task of function tagging, which tags the function
of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging
and chunking information, we use Naive Bayesian theory to disambiguate the
possible function tags of a word. We apply context free grammar (CFG) to find
out the grammatical relations of the function tags. We also create a functional
annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar
sentences. Experiments show that our analysis achieves a good result with
simple sentences and complex sentences.Comment: 16 pages, 7 figures, 8 tables, AIAA-2011 (India). arXiv admin note:
text overlap with arXiv:0912.1820 by other author
Optimal-Time Text Indexing in BWT-runs Bounded Space
Indexing highly repetitive texts --- such as genomic databases, software
repositories and versioned text collections --- has become an important problem
since the turn of the millennium. A relevant compressibility measure for
repetitive texts is , the number of runs in their Burrows-Wheeler Transform
(BWT). One of the earliest indexes for repetitive collections, the Run-Length
FM-index, used space and was able to efficiently count the number of
occurrences of a pattern of length in the text (in loglogarithmic time per
pattern symbol, with current techniques). However, it was unable to locate the
positions of those occurrences efficiently within a space bounded in terms of
. Since then, a number of other indexes with space bounded by other measures
of repetitiveness --- the number of phrases in the Lempel-Ziv parse, the size
of the smallest grammar generating the text, the size of the smallest automaton
recognizing the text factors --- have been proposed for efficiently locating,
but not directly counting, the occurrences of a pattern. In this paper we close
this long-standing problem, showing how to extend the Run-Length FM-index so
that it can locate the occurrences efficiently within space (in
loglogarithmic time each), and reaching optimal time within
space, on a RAM machine of bits. Within
space, our index can also count in optimal time .
Raising the space to , we support count and locate in
and time, which is optimal in the
packed setting and had not been obtained before in compressed space. We also
describe a structure using space that replaces the text and
extracts any text substring of length in almost-optimal time
. (...continues...
Lempel-Ziv Parsing for Sequences of Blocks
The Lempel-Ziv parsing (LZ77) is a widely popular construction lying at the heart of many compression algorithms. These algorithms usually treat the data as a sequence of bytes, i.e., blocks of fixed length 8. Another common option is to view the data as a sequence of bits. We investigate the following natural question: what is the relationship between the LZ77 parsings of the same data interpreted as a sequence of fixed-length blocks and as a sequence of bits (or other âelementaryâ letters)? In this paper, we prove that, for any integer b>1, the number z of phrases in the LZ77 parsing of a string of length n and the number zb of phrases in the LZ77 parsing of the same string in which blocks of length b are interpreted as separate letters (e.g., b=8 in case of bytes) are related as zb=O(bzlognz). The bound holds for both âoverlappingâ and ânon-overlappingâ versions of LZ77. Further, we establish a tight bound zb=O(bz) for the special case when each phrase in the LZ77 parsing of the string has a âphrase-alignedâ earlier occurrence (an occurrence equal to the concatenation of consecutive phrases). The latter is an important particular case of parsing produced, for instance, by grammar-based compression methods
Lempel-Ziv Parsing for Sequences of Blocks
The Lempel-Ziv parsing (LZ77) is a widely popular construction lying at the heart of many compression algorithms. These algorithms usually treat the data as a sequence of bytes, i.e., blocks of fixed length 8. Another common option is to view the data as a sequence of bits. We investigate the following natural question: what is the relationship between the LZ77 parsings of the same data interpreted as a sequence of fixed-length blocks and as a sequence of bits (or other âelementaryâ letters)? In this paper, we prove that, for any integer b>1, the number z of phrases in the LZ77 parsing of a string of length n and the number zb of phrases in the LZ77 parsing of the same string in which blocks of length b are interpreted as separate letters (e.g., b=8 in case of bytes) are related as zb=O(bzlognz). The bound holds for both âoverlappingâ and ânon-overlappingâ versions of LZ77. Further, we establish a tight bound zb=O(bz) for the special case when each phrase in the LZ77 parsing of the string has a âphrase-alignedâ earlier occurrence (an occurrence equal to the concatenation of consecutive phrases). The latter is an important particular case of parsing produced, for instance, by grammar-based compression methods
Massiv-Parallele Algorithmen zum Laden von Daten auf Moderner Hardware
While systems face an ever-growing amount of data that needs to be ingested, queried and analysed, processors are seeing only moderate improvements in sequential processing performance. This thesis addresses the fundamental shift towards increasingly parallel processors and contributes multiple massively parallel algorithms to accelerate different stages of the ingestion pipeline, such as data parsing and sorting.Systeme sehen sich mit einer stetig anwachsenden Menge an Daten konfrontiert, die geladen und analysiert, sowie Anfragen darauf bearbeitet werden mĂźssen. Gleichzeitig nimmt die sequentielle Verarbeitungsgeschwindigkeit von Prozessoren nur noch moderat zu. Diese Arbeit adressiert den Wandel hin zu zunehmend parallelen Prozessoren und leistet mit mehreren massiv-parallelen Algorithmen einen Beitrag um unterschiedliche Phasen der Datenverarbeitung wie zum Beispiel Parsing und Sortierung zu beschleunigen
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