Entry Benajiba:2009:MBS from talip.bib

Last update: Sun Oct 15 02:55:04 MDT 2017                Valid HTML 3.2!

Index sections

Top | Symbols | Numbers | Math | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

BibTeX entry

@Article{Benajiba:2009:MBS,
  author =       "Yassine Benajiba and Imed Zitouni",
  title =        "Morphology-Based Segmentation Combination for {Arabic}
                 Mention Detection",
  journal =      j-TALIP,
  volume =       "8",
  number =       "4",
  pages =        "16:1--16:??",
  month =        dec,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1644879.1644883",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Mon Mar 29 15:37:17 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "The Arabic language has a very rich/complex
                 morphology. Each Arabic word is composed of zero or
                 more {\em prefixes}, one {\em stem\/} and zero or more
                 {\em suffixes}. Consequently, the Arabic data is sparse
                 compared to other languages such as English, and it is
                 necessary to conduct word segmentation before any
                 natural language processing task. Therefore, the
                 word-segmentation step is worth a deeper study since it
                 is a preprocessing step which shall have a significant
                 impact on all the steps coming afterward. In this
                 article, we present an Arabic mention detection system
                 that has very competitive results in the recent
                 Automatic Content Extraction (ACE) evaluation campaign.
                 We investigate the impact of different segmentation
                 schemes on Arabic mention detection systems and we show
                 how these systems may benefit from more than one
                 segmentation scheme. We report the performance of
                 several mention detection models using different kinds
                 of possible and known segmentation schemes for Arabic
                 text: punctuation separation, Arabic Treebank, and
                 morphological and character-level segmentations. We
                 show that the combination of competitive segmentation
                 styles leads to a better performance. Results indicate
                 a statistically significant improvement when Arabic
                 Treebank and morphological segmentations are
                 combined.",
  acknowledgement = ack-nhfb,
  articleno =    "16",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
  keywords =     "Arabic information extraction; Arabic mention
                 detection; Arabic segmentation",
}

Related entries