| other,6-2-P03-1033,ak | previous studies that focus on <term> user 's | knowledge | </term> or typical kinds of <term> users </term> | #4308 Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive. | |
| other,17-3-P03-1033,ak | level </term> to the <term> system </term> , <term> | knowledge | level </term> on the <term> target domain </term> | #4341 Specifically, we set up three dimensions of user models: skill level to the system,knowledge level on the target domain and the degree of hastiness. | |
| tech,14-1-I05-4007,ak | play a crucial role in <term> multilingual | knowledge | processing </term> . Since there is no <term> | #7070 Parallel wordnets built upon correspondences between different languages can play a crucial role in multilingual knowledge processing. | |
| bibliographic citations , small amounts of prior | knowledge | can be used to learn effective models in | #9057 We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. | ||
| learned structure by exploiting simple prior | knowledge | of the desired solutions . In both domains | #9119 However, one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions. | ||
| other,7-2-P05-1053,ak | diverse <term> lexical , syntactic and semantic | knowledge | </term> in <term> feature-based relation extraction | #9285 This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. | |
| other,22-2-P05-1056,ak | that integrate <term> textual and prosodic | knowledge | sources </term> for detecting <term> sentence | #9473 In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. | |
| other,14-6-P05-1056,ak | strengths and weaknesses for modeling the <term> | knowledge | sources </term> . We present a framework | #9593 This probably occurs because each model has different strengths and weaknesses for modeling theknowledge sources. | |
| other,1-2-P05-1057,ak | <term> log-linear models </term> . All <term> | knowledge | sources </term> are treated as <term> feature | #9609 Allknowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible additional variables. | |
| efficiently but also to gain a variety of | knowledge | through reading . The reading materials | #10845 The organized reading materials would enable learners not only to study the target vocabulary efficiently but also to gain a variety of knowledge through reading. | ||
| other,18-2-E06-1041,ak | structure is imposed on the available <term> | knowledge | </term> prior to <term> content determination | #11634 Both problems, it is argued, can be resolved if some structure is imposed on the availableknowledge prior to content determination. | |
| other,30-4-T78-1028,ak | inference </term> depends on the person 's <term> | knowledge | </term> about his own <term> knowledge </term> | #12931 The theory consists of a dimensionalized space of different inference types and their certainty conditions, including a variety of meta-inference types where the inference depends on the person'sknowledge about his own knowledge. | |
| other,34-4-T78-1028,ak | <term> knowledge </term> about his own <term> | knowledge | </term> . The protocols from people 's answers | #12935 The theory consists of a dimensionalized space of different inference types and their certainty conditions, including a variety of meta-inference types where the inference depends on the person's knowledge about his ownknowledge. | |
| interaction </term> ; as a device combining | knowledge | about <term> dialog schemata </term> and about | #13342 as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs. | ||
| about <term> verbal interaction </term> with | knowledge | about <term> task-oriented and goal-directed | #13351 as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs. | ||
| other,31-2-P81-1032,ak | ability to bring <term> task-specific domain | knowledge | </term> ( in addition to <term> general linguistic | #13779 Although single-strategy parsers have met with a measure of success, a multi-strategy approach is shown to provide a much higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge (in addition to general linguistic knowledge) to bear on both grammatical and ungrammatical input. | |
| other,38-2-P81-1032,ak | ( in addition to <term> general linguistic | knowledge | </term> ) to bear on both <term> grammatical | #13786 Although single-strategy parsers have met with a measure of success, a multi-strategy approach is shown to provide a much higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge (in addition to general linguistic knowledge) to bear on both grammatical and ungrammatical input. | |
| other,9-4-P81-1032,ak | </term> exploits different types of <term> | knowledge | </term> ; and their combination provides | #13823 Each of these parsing strategies exploits different types ofknowledge; and their combination provides a strong framework in which to process conjunctions, fragmentary input, and ungrammatical structures, as well as less exotic, grammatically correct input. | |
| <term> expectations </term> , based both on | knowledge | of surface English and on <term> world knowledge | #14330 Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. | ||
| other,21-3-P82-1035,ak | knowledge of surface English and on <term> world | knowledge | </term> of the situation being described | #14337 Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. |
