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Unsupervised Extraction of Recurring Words from Infant-Directed Speech
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In: http://www.cstr.inf.ed.ac.uk/downloads/publications/2011/mcinnes_cogsci2011.pdf
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42 |
Unsupervised Extraction of Recurring Words from Infant-Directed Speech
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In: http://homepages.inf.ed.ac.uk/sgwater/papers/cogsci11_wordextract.pdf
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43 |
Generality for Multi-language of Word Segmentation Method Using Inductive Learning
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In: http://www.afnlp.org/archives/pacling2001/pdf/wang-z.pdf
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Abstract:
We have proposed a method of word segmentation for non-segmented language using inductive learning. We use only surface information of a character string, so that the method has an advantage that is entirely not dependent on any specific language. We have con-firmed its effectiveness for Japanese and Chinese word segmentation respectively. In this paper, we will dis-cuss the generality of our proposed method for multi-language. We used a large amount of experimental data from Japanese corpus EDR and Chinese corpus Sinica to carry out the evaluation experiments. For these two kinds of language that they are quite differ-ent on grammar, structure and morphology, we have used the same algorithm to carry out the evaluation experiments. The experimental results show our pro-posed method is effective for Japanese and Chinese word segmentation, and it is possible to be used to multi-language.
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Keyword:
inductive learning; multi-language; word segmentation
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URL: http://www.afnlp.org/archives/pacling2001/pdf/wang-z.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.579.7212
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44 |
Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One? F∗
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In: http://www.nlpr.ia.ac.cn/cip/zongpublications/2009/2009 paclic23-827-834-wang kun.pdf
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RESEARCH ARTICLE OPEN ACCESS Extract the Punjabi Word from Machine Printed Document Images
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In: http://www.ijera.com/papers/Vol3_issue5/BJ35343348.pdf
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Segmentation of Printed Text in Devanagari Script and Gurmukhi Script Vijay Kumar ECD, Deptt.
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In: http://www.ijcaonline.org/volume3/number8/pxc3871058.pdf
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47 |
A Word-Finding Automaton for Chinese Sentence Tokenization
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In: http://cslp.comp.nus.edu.sg/luakt/paper/SUB01.ps
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48 |
Line and Word Segmentation of . . .
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In: http://www.iit.demokritos.gr/~bgat/cr1115.pdf
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49 |
Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One? F∗
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In: http://www.aclweb.org/anthology-new/Y/Y09/Y09-2047.pdf
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50 |
Comparing Different Criteria for Vietnamese Word Segmentation
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In: http://www.aclweb.org/anthology/W/W12/W12-5005.pdf
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51 |
A Pragmatic Chinese Word Segmentation Approach Based on Mixing Models 1
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In: http://www.aclclp.org.tw/clclp/v11n4/v11n4a5.pdf
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52 |
Strategies of Processing Japanese Names and Character Variants in Traditional Chinese Text
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In: http://wing.comp.nus.edu.sg/~antho/O/O12/O12-4004.pdf
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53 |
Using Heuristics Based Approach for Segmentation and Recognition of Printed Arabic Characters
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In: http://www.sersc.org/journals/IJAST/vol49/2.pdf
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Chinese Word Auto-Confirmation System using a Hybrid Approach Abstract
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In: http://www.iis.sinica.edu.tw/IASL/webpdf/paper-2003-Chinese_Word_Auto-Confirmation_System_using_a_Hybrid_Approach.pdf
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Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems
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In: http://www.lscp.net/persons/dupoux/papers/Ludusan_VJGCJD_2014_Evaluation_toolbox_term_discovery.LREC.pdf
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© The Association for Computational Linguistics and Chinese Language Processing Chinese Word Segmentation as Character Tagging
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In: http://www.aclclp.org.tw/clclp/v8n1/v8n1a2.pdf
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Ó The Association for Computational Linguistics and Chinese Language Processing
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In: http://verbs.colorado.edu/~xuen/publications/clclp03.xue.pdf
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Storage and Retrieval]: Content Analysis and Indexing – Linguistic processing.
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In: http://www2007.org/posters/poster923.pdf
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Speech- and sound-segmentation in dyslexia: evidence for a multiple-level cortical impairment
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In: http://neuroscience.aecom.yu.edu/labs/sussmanlab/Pubs/Kujala_dyslexia.pdf
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60 |
It’s
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In: http://www.socsci.uci.edu/~lpearl/papers/PhillipsPearl2014_ReasonableErrorsWordSegCogSci.pdf
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