| . In this presentation , we describe the | features | of and requirements for a genuinely useful | #260 In this presentation, we describe the features of and requirements for a genuinely useful software infrastructure for this purpose. | ||
| called a <term> semantic frame </term> . The key | features | of the <term> system </term> include : ( i | #441 The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments). | ||
| other,7-2-P01-1070,ak | which are built from <term> shallow linguistic | features | </term> of questions , are employed to predict | #2153 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. | |
| other,36-1-N03-1033,ak | </term> , ( ii ) broad use of <term> lexical | features | </term> , including jointly conditioning | #2947 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| other,66-1-N03-1033,ak | fine-grained modeling of <term> unknown word | features | </term> . Using these ideas together , the | #2978 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| other,12-3-P03-1002,ak | based on : ( 1 ) an extended set of <term> | features | </term> ; and ( 2 ) <term> inductive decision | #3763 It is based on: (1) an extended set offeatures; and (2) inductive decision tree learning. | |
| other,5-3-P03-1022,ak | non-NP-antecedents </term> . We present a set of <term> | features | </term> designed for <term> pronoun resolution | #4004 We present a set offeatures designed for pronoun resolution in spoken dialogue and determine the most promising features. | |
| other,18-3-P03-1022,ak | </term> and determine the most promising <term> | features | </term> . We evaluate the system on twenty | #4017 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promisingfeatures. | |
| techniques </term> are able to produce useful | features | for <term> paraphrase classification </term> | #7459 Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. | ||
| other,14-3-J05-1003,ak | <term> ranking </term> , using additional <term> | features | </term> of the <term> tree </term> as evidence | #8068 A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence. | |
| other,19-4-J05-1003,ak | represented as an arbitrary set of <term> | features | </term> , without concerns about how these | #8094 The strength of our approach is that it allows a tree to be represented as an arbitrary set offeatures, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. | |
| other,26-4-J05-1003,ak | , without concerns about how these <term> | features | </term> interact or overlap and without the | #8101 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how thesefeatures interact or overlap and without the need to define a derivation or a generative model which takes these features into account. | |
| other,45-4-J05-1003,ak | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #8120 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes thesefeatures into account. | |
| other,23-7-J05-1003,ak | evidence from an additional 500,000 <term> | features | </term> over <term> parse trees </term> that | #8187 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000features over parse trees that were not included in the original model. | |
| other,19-9-J05-1003,ak | of the <term> sparsity </term> of the <term> | feature | space </term> in the <term> parsing data </term> | #8245 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of thefeature space in the parsing data. | |
| tech,21-11-J05-1003,ak | simplicity and efficiency — to work on <term> | feature | selection methods </term> within <term> log-linear | #8291 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models. | |
| tech,40-5-P05-1010,ak | </term> created using extensive <term> manual | feature | selection </term> . This paper considers | #8589 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 extensive manual feature selection. | |
| other,11-6-P05-1053,ak | effective incorporation of diverse <term> | features | </term> enables our system outperform previously | #9390 Evaluation on the ACE corpus shows that effective incorporation of diversefeatures enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types. | |
| tech,6-2-P05-1057,ak | knowledge sources </term> are treated as <term> | feature | functions </term> , which depend on the <term> | #9614 All knowledge sources are treated asfeature functions, which depend on the source language sentence, the target language sentence and possible additional variables. | |
| other,20-4-P05-1057,ak | bilingual dictionary coverage </term> as <term> | features | </term> . Our experiments show that <term> | #9669 In this paper, we use IBM Model 3 alignment properties, POS correspondence, and bilingual dictionary coverage asfeatures. |
