| concatenation languages [ RCL ] </term> can be | parsed | in <term> polynomial time </term> and many <term> | #1632 In particular, range concatenation languages [RCL] can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent RCGs without increasing their worst-case parsing time complexity. | ||
| <term> tree adjoining grammar </term> can be | parsed | in O ( n6 ) <term> time </term> . In this paper | #1671 For example, after translation into an equivalent RCG, any tree adjoining grammar can be parsed in O(n6) time. | ||
| tech,10-2-N03-1026,ak | LFG </term> , a <term> transfer component for | parse | reduction </term> operating on <term> packed | #2822 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. | |
| other,17-2-N03-1026,ak | reduction </term> operating on <term> packed | parse | forests </term> , and a <term> maximum-entropy | #2827 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. | |
| tech,10-5-H05-1064,ak | , we apply the <term> model </term> to <term> | parse | reranking </term> . The <term> model </term> | #5524 As a case study, we apply the model toparse reranking. | |
| other,18-7-H05-1064,ak | naturally to NLP structures other than <term> | parse | trees </term> . This paper presents a <term> | #5578 Although our experiments are focused on parsing, the techniques described generalize naturally to NLP structures other thanparse trees. | |
| other,7-2-J05-1003,ak | parser </term> produces a set of <term> candidate | parses | </term> for each <term> input sentence </term> | #8036 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. | |
| other,24-2-J05-1003,ak | initial <term> ranking </term> of these <term> | parses | </term> . A second <term> model </term> then | #8052 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses. | |
| apply the <term> boosting method </term> to | parsing | the <term> Wall Street Journal treebank </term> | #8157 We apply the boosting method to parsing the Wall Street Journal treebank. | ||
| other,25-7-J05-1003,ak | additional 500,000 <term> features </term> over <term> | parse | trees </term> that were not included in the | #8189 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features overparse trees that were not included in the original model. | |
| other,8-1-P05-1010,ak | generative probabilistic model </term> of <term> | parse | trees </term> , which we call <term> PCFG-LA | #8487 This paper defines a generative probabilistic model ofparse trees, which we call PCFG-LA. | |
| lr,8-3-P05-1010,ak | </term> are automatically induced from a <term> | parsed | corpus </term> by training a <term> PCFG-LA | #8520 Fine-grained CFG rules are automatically induced from aparsed corpus by training a PCFG-LA model using an EM-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 | #8533 Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. | |
| other,8-3-P05-1034,ak | </term> , project the <term> source dependency | parse | </term> onto the <term> target sentence </term> | #8892 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. | |
| model,8-4-P05-1053,ak | most of useful information in <term> full | parse | trees </term> for <term> relation extraction | #9339 This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. | |
| other,25-4-P05-1073,ak | <term> classifier </term> for <term> gold-standard | parse | trees </term> on <term> PropBank </term> . Previous | #10138 This system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank. | |
| tech,9-4-P80-1026,ak | In this paper , we outline a set of <term> | parsing | flexibilities </term> that such a <term> system | #13684 In this paper, we outline a set ofparsing flexibilities that such a system should provide. | |
| other,33-6-P84-1047,ak | structure </term> , and worked examples of <term> | parses | </term> . A <term> parser </term> incorporating | #15153 Representative samples from an entity-oriented language definition are presented, along with a control structure for an entity-oriented parser, some parsing strategies that use the control structure, and worked examples ofparses. | |
| tech,15-1-C88-1066,ak | Restrictions ( CCRs ) </term> and describes two <term> | parsing | algorithms </term> that interpret it . <term> | #18102 This paper summarizes the formalism of Category Cooccurrence Restrictions (CCRs) and describes twoparsing algorithms that interpret it. | |
| second stage the <term> sentence </term> is | parsed | with respect to this set . An <term> Earley-type | #19713 In the first stage, the parser selects a set of elementary structures associated with the lexical items in the input sentence, and in the second stage the sentence is parsed with respect to this set. |
