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About 10,000 papers for “text compression”
Berlin Chen · 2016
– Noun words (or group of noun words) are more representative of the semantics of a doc content – Preprocess the text of docs in collection in order to select the meaningful/representative index terms • Control the size of the vocabulary • Text Operations – During the preprocessing phrase, a few useful text operations can be performed
Cited by 0
SpringerReference · 2011
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Amar Mukherjee, Fauzia Salim Awan · Elsevier eBooks · 2003
Cited by 4
Andrew Davison · ACM SIGACT News · 1993
A new approach to the "lossy" compression of text is described, based on the computational benefits of reading handwritten text, as opposed to text set in a more formal typographical style. Specifically, a new class of fonts, called vague fonts , are proposed which encode the vagueness of handwriting. Text written using a vague font can be compressed to a much greater degree than the same text represented in a conventional font.
Cited by 0Open access
Sajilal Divakaran, C. L. Biji, C Anjali et al. · International Journal of Information Systems and Engineering · 2013
Cited by 4Open access
Ondřej Kazı́k, Jan Lánský · Digital Repository (National Repository of Grey Literature) · 2011
The compression of texts written in natural language can exploit information about its linguistic character. It is shown that separation of coding of part-of-speech tags of a sentence (type of sentence) from the text alone can improve resulting compression ratio. For this purpose the tagging method NNTagger based on neural networks is designed. This thesis is focused on speci fication of the compression model of texts written in Czech. We propose methods of constructing of initial dictionaries and test their influence on the compression ratio.
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Jon Radue · Unisa Institutional Repository (University of South Africa) · 1979
The benefits associated with text compression include more efficient use of peripheral devices. faster information transfer rates and, in some cases, \nimproved sorting speeds through the use of shorter sort keys. However. these advantages must be balanced against a slight increase in CPU-time and \nthe extra storage required for the associated code tables. \nInformation theory and statistics of English provide a background for the discussion of various text compression algorithms. The common \nobjective of the methods described is to reduce the physical size of the text file while maintaining a complete representation of the information \n(reversible compression). The methods can be divided into two main classes: \n( a) those that re-define the symbol codes to more accurate reflect the information content of each symbol, and \n(b) those that use special codes to represent commonly occurring groups of symbols, thereby reducing redundancy due to mutual information between \nsymbols. \nCompression techniques not covered include those dealing with data files and with telemetry. \nFinally. another approach to compression is described. which also holds some promise for automatic indexing and simpler inverted file design in \ndocument retrieval systems.
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Igor A. Bolshakov, Alexandre Smirnov · Journal of Mathematical Sciences · 1991
Cited by 1
Bell, Timothy C., Witten, Ian H., Cleary, John G. · PRISM (University of Calgary) · 1988
The best schemes for text compression employ large models to help them predict which characters will come next. The actual next characters are coded with respect to the prediction, resulting in compression of information. Models are best formed adaptively, based on the text seen so far. This paper surveys successful strategies for adaptive modeling which are suitable for use in practical text compression systems. The strategies fall into three main classes: finite-context modeling, in which the last few characters are used to condition the probability distribution for the next one; finite-state modeling, in which the distribution is conditioned by the current state (and which subsumes finite-context modeling as an important special case); and dictionary modeling, in which strings of characters are replaced by pointers into an evolving dictionary. A comparison of different methods on the same sample texts is included, along with an analysis of future research directions.
Cited by 0Open access
Suher man, Andysah Putera Utama Siahaan · International Journal of Computer Science and Engineering · 2016
Huffman is one of the compression algorithms.It is the most famous algorithm to compress text.There are four phases in the Huffman algorithm to compress text.The first is to group the characters.The second is to build the Huffman tree.The third is the encoding, and the last one is the construction of coded bits.The Huffman algorithm principle is the character that often appears on encoding with a series of short bits and characters that rarely appeared in bit-encoding with a longer series.Huffman compression technique can provide savings of 30% from the original bits.It works based on the frequency of characters.The more the similar character reached, the higher the compression rate gained.
Cited by 9Open access
Siahaan, Andysah · Open Science Framework · 2017
Huffman is one of the compression algorithms. It is the most famous algorithm to compress text. There are four phases in the Huffman algorithm to compress text. The first is to group the characters. The second is to build the Huffman tree. The third is the encoding, and the last one is the construction of coded bits. The Huffman algorithm principle is the character that often appears on encoding with a series of short bits and characters that rarely appeared in bit-encoding with a longer series. Huffman compression technique can provide savings of 30% from the original bits. It works based on the frequency of characters. The more the similar character reached, the higher the compression rate gained.
Cited by 0Open access
Maxime Crochemore, Filippo Mignosi, Antonio Restivo et al. · Lecture notes in computer science · 1999
Cited by 50Open access
Kwok-shing Cheng · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · 2001
Cited by 0
S Gowtham, G. Iyshwarya, Kaushik Veluru et al. · 2010
The networking field is looking forward to improved and efficient methods in channel utilization. For some text, data recovery becomes indispensable because of the importance of data it holds. Therefore, a lossless decomposition algorithm which is independent of the nature and pattern of text is today's top concern. Efficiency of algorithms used today varies greatly depending on the nature of text. Such algorithms need some characters to be frequently appearing in the text and randomness in the characters present distorts the consistency to a large extent. This paper brings in the idea of using an art form called ambigram to compress text with consistency in the efficiency of the compression.
Cited by 1
Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro et al. · ACM Transactions on Information Systems · 2010
We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32% and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than that necessary to decompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are preferable in several aspects over the existing adaptive and semistatic compressors for natural language texts.
Cited by 32Open access
Andysah Putera Utama Siahaan · 2017
Huffman is one of the compression algorithms. It is the most famous algorithm to compress text. There are four phases in the Huffman algorithm to compress text. The first is to group the characters. The second is to build the Huffman tree. The third is the encoding, and the last one is the construction of coded bits. The Huffman algorithm principle is the character that often appears on encoding with a series of short bits and characters that rarely appeared in bit-encoding with a longer series. Huffman compression technique can provide savings of 30% from the original bits. It works based on the frequency of characters. The more the similar character reached, the higher the compression rate gained.
Cited by 8
Tim Bell, Ian H. Witten, John G. Cleary · ACM Computing Surveys · 1989
The best schemes for text compression use large models to help them predict which characters will come next. The actual next characters are coded with respect to the prediction, resulting in compression of information. Models are best formed adaptively, based on the text seen so far. This paper surveys successful strategies for adaptive modeling that are suitable for use in practical text compression systems. The strategies fall into three main classes: finite-context modeling, in which the last few characters are used to condition the probability distribution for the next one; finite-state modeling, in which the distribution is conditioned by the current state (and which subsumes finite-context modeling as an important special case); and dictionary modeling, in which strings of characters are replaced by pointers into an evolving dictionary. A comparison of different methods on the same sample texts is included, along with an analysis of future research directions.
Cited by 262Open access
Siahaan, Andysah Putera Utama · OSF Preprints (OSF Preprints) · 2016
Huffman is one of the compression algorithms. It is the most famous algorithm to compress text. There are four phases in the Huffman algorithm to compress text. The first is to group the characters. The second is to build the Huffman tree. The third is the encoding, and the last one is the construction of coded bits. The Huffman algorithm principle is the character that often appears on encoding with a series of short bits and characters that rarely appeared in bit-encoding with a longer series. Huffman compression technique can provide savings of 30% from the original bits. It works based on the frequency of characters. The more the similar character reached, the higher the compression rate gained.
Cited by 0Open access
Mikhail J. Atallah, Marina Blanton · 2009
Cited by 0
Venka Palaniappan, Shahram Latifi · 2007
Cited by 8