[Reference] Introduction of Logic in Language Modelling: The Minimum Perplexity Criterion

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Bouchaffra</span></p> <p style="TEXT-ALIGN: center"></p> <h3 class="underlinedhead" style="font-size:x-large">References</h3> <p class="first_paragraph" style="font-size:large"><span id="lblReferences" style="font-size:large"> <ol style="list-style:none; margin: 0em; padding: 0em;"> <li>[1] S.M. Katz, Estimation of probabilities from sparse data forthe language model component of a speech recognizer, IEEETrans. on Acoustics, Speech and Signal Processing, 35(3), 1981,400–401. <small><a href="https://dx.doi.org/10.1109/TASSP.1987.1165125">doi:10.1109/TASSP.1987.1165125</a></small></li> <li>[2] P.F. Brown, V.J. Della Pietra, P.V. deSouza, J.C. Lai, &R.L. Mercer, Class-based n-grams models of natural language,Computational Linguistics, 18(14), 1992, 467–479.</li> <li>[3] T. Jaakola, M. Meila, & T. Jebara, Maximum entropy discrimination, in S.A. Solla, T.K. Leen, & K.R. Muller (Eds.),Advances in Neural Information Processing Systems, Vol. 12(Cambridge, MA: MIT Press, 1999).</li> <li>[4] P. Clarkson, Adaptation of statistical language models for automatic speech recognition, doctoral diss., Cambridge University,1999.</li> <li>[5] R. Rosenfeld, Adaptive statistical language modeling: A maximum entropy approach, doctoral diss., Carnegie-Mellon University, Pittsburgh, 1994.</li> <li>[6] J. Bellegarda, Exploiting latent semantic information in statistical language modelling, Proc. IEEE, 88(8), 2000, 1279–1296. <small><a href="https://dx.doi.org/10.1109/5.880084">doi:10.1109/5.880084</a></small></li> <li>[7] Y. Bengio, R. Ducharme, & P. Vincent, A neural probabilisticlanguage model, in T.K. Leen, T. Dietterich, & V. Tresp(Eds.), Advances in Neural Information Processing Systems,Vol. 13 (Cambridge, MA: MIT Press, 2001).</li> <li>[8] K. Church & W. Gale, Enhanced Good Turing and Cat-Cal: Two new methods for estimating probabilities of Englishbigrams, in E. Briscoe (Ed.), Computer Speech and Language(Amsterdam: Elsevier, 1990).</li> <li>[9] I. Dagan, F. Pereira, & L. Lee, Similarity-based estimation ofword co-occurrence probabilities, Proc. 32nd Annual Meetingof the Association for Computational Linguistics. New MexicoState University, June 1994, Las Cruces, 272–278.</li> <li>[10] F. Jelinek, R. Mercer, & S. Roukos, Principles of lexicallanguage modelling for speech recognition, in S. Furui &M. Moham Sondhi (Eds.), Advances in speech signal processing(New York: Mercer Dekker, 1992), 651–699.</li> <li>[11] A. Nadas, Estimation of probabilities in the language modelof the IBM speech recognition system, IEEE Trans. Acoustic,Speech and Signal Processing, 32, 1981, 819–861.</li> <li>[12] D. Bouchaffra, V. Govindaraju, & S.N. Srihari, Postprocessing of recognized strings using nonstationary Markovian mod-197els, IEEE Trans. Pattern Analysis and Machine Intelligence(PAMI), 21(10), 1999, 990–999. <small><a href="https://dx.doi.org/10.1109/34.799906">doi:10.1109/34.799906</a></small></li> <li>[13] D. Bouchaffra, E. Koontz, V. Kripasundar, & R.K. Srihari,Integrating signal and language context to improve handwritten phrase recognition: Alternative approaches, in Preliminary Papers of the Sixth International Workshop on ArtiﬁcialIntelligence and Statistics, Fort Lauderdale, FL, 1997, 47.</li> <li>[14] D. Bouchaffra, E. Koontz, V. Kripasundar, & R.K. Srihari, Incorporating diverse information sources in handwriting recognition postprocessing, International Journal of Imaging Systemsand Technology, 7, 1996, 320–329. <small><a href="https://dx.doi.org/10.1002/(SICI)1098-1098(199624)7:4<320::AID-IMA7>3.0.CO;2-A">doi:10.1002/(SICI)1098-1098(199624)7:4<320::AID-IMA7>3.0.CO;2-A</a></small></li> <li>[15] I.J. Good, The population frequencies of species and theestimation of population parameters, Biometrika, 40, 1953,237–264. <small><a href="https://dx.doi.org/10.2307/2333344">doi:10.2307/2333344</a></small></li> <li>[16] S.F. Chen & R. Rosenfeld, A survey of smoothing techniquesfor ME models, IEEE Trans. on Speech and Audio Processing,8(1), 2000, 3750. <small><a href="https://dx.doi.org/10.1109/89.817452">doi:10.1109/89.817452</a></small></li> <li>[17] N.J. Nilsson, Probabilistic logic, Journal of Artiﬁcial Intelligence, 281, 1981, 71–81.</li> <li>[18] D. Bouchaffra, Theory and algorithms for analysing the consistent region in probabilistic logic, An International Journalof Computers & Mathematics, 25(3), 1993, 13, 25.</li> <li>[19] S. Russel & P. Norvig, Artiﬁcial intelligence: A modernapproach (New Jersey: Prentice Hall, 1995).</li> <li>[20] J. Dieudonné, Foundations of modern analysis (New York:Academic Press, 1960).</li> </ol></span></p> <p style="font-size:x-large">Important Links:</p> <ul> <li> <span id="lblAbstract"><a href="Abstract.aspx?PaperId=20689">Abstract</a></span>  <li> <span style="font-size:large">DOI:</span> <span id="lblDOI" style="font-size:large"><a href="https://dx.doi.org/10.2316/Journal.206.2005.3.206-2840">10.2316/Journal.206.2005.3.206-2840</a></span> <li> <span id="lblParent" style="font-size:large"><i>From Journal</i> <a href='Content_Of_Journal.aspx?JournalID=62'>(206) International Journal of Robotics and Automation - 2005</a></span></li> </ul> <p><input type="submit" name="btnCart" value="Add Paper to Cart" id="btnCart" style="font-size:large" /></p> <p><a href="#" style="font-size:large" onclick="JavaScript:window.history.back();return false;">Go Back</a></p> </form> </div> <script> elements = (Array.from(document.getElementsByTagName("li"))); for(i in elements){ element = elements[i]; if(!element.className && !element.id){ element.style.fontSize = 'large'; } } let regexp = `${String.fromCharCode(8727)}+[,]?`; console.log(regexp); document.getElementById("lblAuthors").innerText = document.getElementById("lblAuthors").innerText.replace(new RegExp(regexp, 'g'), ""); </script> <div style="clear:both;"></div> <div id="footer"> <script type="text/javascript" src="https://apis.google.com/js/plusone.js"></script> <g:plusone></g:plusone>  <a href="https://www.linkedin.com/in/acta-press-a7060923a" class="socialIcon Linkein-Icon" target="_blank" title="LinkedIn"> <img src="../images/linkedin-icon.png" alt="LinkedIn" border="0" style="float:none;border:0;margin-left:0px;margin-top:0px;"/> </a> <a href="privacy.aspx">Privacy & Legal</a> <a href="sitemap.aspx">Sitemap</a> <a href="privacy.aspx#copyrighttitle">Copyright © 2025 ACTA Press</a> </div> <p style="margin:0.5em; float: left">  </p> </div> </div> <script type="text/javascript"> if(window.location.protocol == "https:") { if(typeof COT === "function") COT("http://www.actapress.com/images/cot.gif", "SC2", "none"); } </script> <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-7576801-1']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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[Reference] Introduction of Logic in Language Modelling: The Minimum Perplexity Criterion