| other,6-2-P05-1010,ak | <term> model </term> is an extension of <term> | PCFG | </term> in which <term> non-terminal symbols | #8501 This model is an extension ofPCFG in which non-terminal symbols are augmented with latent variables. | |
| tech,17-3-P05-1010,ak | <term> PCFG-LA model </term> using an <term> | EM-algorithm | </term> . Because <term> exact parsing </term> | #8529 Fine-grained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using anEM-algorithm. | |
| tech,1-4-P05-1010,ak | <term> EM-algorithm </term> . Because <term> | exact parsing | </term> with a <term> PCFG-LA </term> is NP-hard | #8532 Becauseexact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. | |
| tech,34-5-P05-1010,ak | which is comparable to that of an <term> | unlexicalized PCFG parser | </term> created using extensive <term> manual | #8582 In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6% (Fa5 , sentences a6 40 words), which is comparable to that of anunlexicalized PCFG parser created using extensive manual feature selection. | |
| model,9-5-P05-1010,ak | the <term> Penn WSJ corpus </term> , our <term> | automatically trained model | </term> gave a performance of 86.6 % ( Fa5 | #8557 In experiments using the Penn WSJ corpus, ourautomatically trained model gave a performance of 86.6% (Fa5 , sentences a6 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection. | |
| tech,40-5-P05-1010,ak | parser </term> created using extensive <term> | manual feature selection | </term> . This paper considers the problem | #8588 In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6% (Fa5 , sentences a6 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensivemanual feature selection. | |
| lr-prod,4-5-P05-1010,ak | compared . In experiments using the <term> | Penn WSJ corpus | </term> , our <term> automatically trained | #8552 In experiments using thePenn WSJ corpus, our automatically trained model gave a performance of 86.6% (Fa5 , sentences a6 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection. | |
| other,8-1-P05-1010,ak | generative probabilistic model </term> of <term> | parse trees | </term> , which we call <term> PCFG-LA </term> | #8487 This paper defines a generative probabilistic model ofparse trees, which we call PCFG-LA. | |
| other,14-2-P05-1010,ak | non-terminal symbols </term> are augmented with <term> | latent variables | </term> . <term> Fine-grained CFG rules </term> | #8509 This model is an extension of PCFG in which non-terminal symbols are augmented withlatent variables. | |
| model,13-3-P05-1010,ak | <term> parsed corpus </term> by training a <term> | PCFG-LA model | </term> using an <term> EM-algorithm </term> | #8525 Fine-grained CFG rules are automatically induced from a parsed corpus by training aPCFG-LA model using an EM-algorithm. | |
| other,1-2-P05-1010,ak | we call <term> PCFG-LA </term> . This <term> | model | </term> is an extension of <term> PCFG </term> | #8496 Thismodel is an extension of PCFG in which non-terminal symbols are augmented with latent variables. | |
| model,0-3-P05-1010,ak | with <term> latent variables </term> . <term> | Fine-grained CFG rules | </term> are automatically induced from a <term> | #8512 This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables.Fine-grained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. | |
| lr,8-3-P05-1010,ak | </term> are automatically induced from a <term> | parsed corpus | </term> by training a <term> PCFG-LA model </term> | #8520 Fine-grained CFG rules are automatically induced from aparsed corpus by training a PCFG-LA model using an EM-algorithm. | |
| other,14-1-P05-1010,ak | <term> parse trees </term> , which we call <term> | PCFG-LA | </term> . This <term> model </term> is an extension | #8493 This paper defines a generative probabilistic model of parse trees, which we callPCFG-LA. | |
| other,4-1-P05-1010,ak | are available . This paper defines a <term> | generative probabilistic model | </term> of <term> parse trees </term> , which | #8483 This paper defines agenerative probabilistic model of parse trees, which we call PCFG-LA. | |
| other,5-4-P05-1010,ak | Because <term> exact parsing </term> with a <term> | PCFG-LA | </term> is NP-hard , several approximations | #8536 Because exact parsing with aPCFG-LA is NP-hard, several approximations are described and empirically compared. | |
| other,9-2-P05-1010,ak | extension of <term> PCFG </term> in which <term> | non-terminal symbols | </term> are augmented with <term> latent variables | #8504 This model is an extension of PCFG in whichnon-terminal symbols are augmented with latent variables. |
