Scientific Research An Academic Publisher
OPEN ACCESS
Add your e-mail address to receive free newsletters from SCIRP.
Select Journal AA AAD AAR AASoci AAST ABB ABC ABCR ACES ACS ACT AD ADR AE AER AHS AID AiM AIT AJAC AJC AJCC AJCM AJIBM AJMB AJOR AJPS ALAMT ALC ALS AM AMI AMPC ANP APD APE APM ARS ARSci AS ASM BLR CC CE CellBio ChnStd CM CMB CN CRCM CS CSTA CUS CWEEE Detection EMAE ENG EPE ETSN FMAR FNS GEP GIS GM Graphene GSC Health IB ICA IIM IJAA IJAMSC IJCCE IJCM IJCNS IJG IJIDS IJIS IJMNTA IJMPCERO IJNM IJOC IJOHNS InfraMatics JACEN JAMP JASMI JBBS JBCPR JBiSE JBM JBNB JBPC JCC JCDSA JCPT JCT JDAIP JDM JEAS JECTC JEMAA JEP JFCMV JFRM JGIS JHEPGC JHRSS JIBTVA JILSA JIS JMF JMGBND JMMCE JMP JPEE JQIS JSBS JSEA JSEMAT JSIP JSS JSSM JST JTR JTST JTTs JWARP LCE MC ME MI MME MNSMS MPS MR MRC MRI MSA MSCE NJGC NM NR NS OALib OALibJ ODEM OJA OJAB OJAcct OJAnes OJAP OJApo OJAppS OJAPr OJAS OJBD OJBIPHY OJBM OJC OJCB OJCD OJCE OJCM OJD OJDer OJDM OJE OJEE OJEM OJEMD OJEpi OJER OJF OJFD OJG OJGas OJGen OJI OJIC OJIM OJINM OJL OJM OJMC OJMetal OJMH OJMI OJMIP OJML OJMM OJMN OJMP OJMS OJMSi OJN OJNeph OJO OJOG OJOGas OJOp OJOph OJOPM OJOTS OJPathology OJPC OJPChem OJPed OJPM OJPP OJPS OJPsych OJRA OJRad OJRD OJRM OJS OJSS OJSST OJST OJSTA OJTR OJTS OJU OJVM OPJ POS PP PST PSYCH SAR SCD SGRE SM SN SNL Soft SS TEL TI UOAJ VP WET WJA WJCD WJCMP WJCS WJET WJM WJNS WJNSE WJNST WJV WSN YM
More>>
Church, K.W. and Hanks, P. (1990) Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics, 16, 22-29.
has been cited by the following article:
TITLE: Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation
AUTHORS: Noushin Riahi, Fatemeh Sedghi
KEYWORDS: Collocation Extraction, Word Sense Disambiguation, Untagged Corpus, Decision List
JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.4, April 22, 2016
ABSTRACT: Word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.
Related Articles:
Word Sense Disambiguation in Information Retrieval
Francis de la C. Fernández REYES, Exiquio C. Pérez LEYVA, Rogelio Lau FERNáNDEZ
DOI: 10.4236/iim.2009.12018 5,139 Downloads 9,406 Views Citations
Pub. Date: November 30, 2009
Robust Image Registration Based on Mutual Information Measure
Witold Kosiński, Paweł Michalak, Piotr Gut
DOI: 10.4236/jsip.2012.32023 4,030 Downloads 6,491 Views Citations
Pub. Date: May 30, 2012
Extending Qualitative Probabilistic Network with Mutual Information Weights
Kun Yue, Feng Wang, Mujin Wei, Weiyi Liu
DOI: 10.4236/ijis.2015.53012 2,875 Downloads 3,383 Views Citations
Pub. Date: February 3, 2015
Intelligent Information Management in Aquaponics to Increase Mutual Benefits
Divas Karimanzira, Cai Na, Yaoguang Wei, Mu Hong
DOI: 10.4236/iim.2021.131003 77 Downloads 232 Views Citations
Pub. Date: January 26, 2021
The Computational Theory of Intelligence: Information Entropy
Daniel Kovach
DOI: 10.4236/ijmnta.2014.34020 4,737 Downloads 5,864 Views Citations
Pub. Date: September 29, 2014