Evolution of logic programs: part-of-speech tagging
An algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attr...
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| creator | Reiser, P.G.K. Riddle, P.J. |
| description | An algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features, including the ability to use explicit background (user-supplied) knowledge and to produce comprehensible output. We present results of using the algorithm to a natural language processing problem, part-of-speech tagging. The results indicate that using an evolutionary algorithm to direct a population of ILP learners can increase accuracy. This result is further improved when crossover is used to exchange rules at intermediate stages in learning. The improvement over Progol, a greedy ILP algorithm, is statistically significant (P |
| doi_str_mv | 10.1109/CEC.1999.782604 |
| format | Conference Proceeding |
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The improvement over Progol, a greedy ILP algorithm, is statistically significant (P<0.005).</description><identifier>ISBN: 0780355369</identifier><identifier>ISBN: 9780780355361</identifier><identifier>DOI: 10.1109/CEC.1999.782604</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computer science ; Evolutionary computation ; Genetic algorithms ; Logic programming ; Natural language processing ; Robustness ; Search methods ; Tagging</subject><ispartof>Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. 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It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features, including the ability to use explicit background (user-supplied) knowledge and to produce comprehensible output. We present results of using the algorithm to a natural language processing problem, part-of-speech tagging. The results indicate that using an evolutionary algorithm to direct a population of ILP learners can increase accuracy. This result is further improved when crossover is used to exchange rules at intermediate stages in learning. The improvement over Progol, a greedy ILP algorithm, is statistically significant (P<0.005).</description><subject>Computer science</subject><subject>Evolutionary computation</subject><subject>Genetic algorithms</subject><subject>Logic programming</subject><subject>Natural language processing</subject><subject>Robustness</subject><subject>Search methods</subject><subject>Tagging</subject><isbn>0780355369</isbn><isbn>9780780355361</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01LxDAUAAMiqOueBU_9A615eUmaeJNSP2DBy96XNH2Jke6mpFXw3yusc5nbwDB2B7wB4Pah67sGrLVNa4Tm8oLd8NZwVAq1vWLbZfnkf6CVLYprhv13nr7WlE9VDtWUY_LVXHIs7rg8VrMra51DvcxE_qNaXYzpFG_ZZXDTQtt_b9j-ud93r_Xu_eWte9rVydi1dkRGqpYCKuEVD6NCJKmHQQ-IwXvvsJWSQLigwSltEYIBAXpUCiyNuGH352wiosNc0tGVn8P5Cn8BzbNB_A</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Reiser, P.G.K.</creator><creator>Riddle, P.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1999</creationdate><title>Evolution of logic programs: part-of-speech tagging</title><author>Reiser, P.G.K. ; Riddle, P.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i89t-aee8457ef352c50fd533e46bb6b33fccca3744e12af61a56931f81216d5519ed3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Computer science</topic><topic>Evolutionary computation</topic><topic>Genetic algorithms</topic><topic>Logic programming</topic><topic>Natural language processing</topic><topic>Robustness</topic><topic>Search methods</topic><topic>Tagging</topic><toplevel>online_resources</toplevel><creatorcontrib>Reiser, P.G.K.</creatorcontrib><creatorcontrib>Riddle, P.J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Reiser, P.G.K.</au><au>Riddle, P.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evolution of logic programs: part-of-speech tagging</atitle><btitle>Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)</btitle><stitle>CEC</stitle><date>1999</date><risdate>1999</risdate><volume>2</volume><spage>1338</spage><epage>1345 Vol. 2</epage><pages>1338-1345 Vol. 2</pages><isbn>0780355369</isbn><isbn>9780780355361</isbn><abstract>An algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and negative examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features, including the ability to use explicit background (user-supplied) knowledge and to produce comprehensible output. We present results of using the algorithm to a natural language processing problem, part-of-speech tagging. The results indicate that using an evolutionary algorithm to direct a population of ILP learners can increase accuracy. This result is further improved when crossover is used to exchange rules at intermediate stages in learning. The improvement over Progol, a greedy ILP algorithm, is statistically significant (P<0.005).</abstract><pub>IEEE</pub><doi>10.1109/CEC.1999.782604</doi></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science Evolutionary computation Genetic algorithms Logic programming Natural language processing Robustness Search methods Tagging |
| title | Evolution of logic programs: part-of-speech tagging |
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