| carries important information yet it is also | time | consuming to document . Given the development | #12 Oral communication is ubiquitous and carries important information yet it is also time consuming to document. | ||
| additional <term> indices </term> such as the | time | and place of the rejoinder and the attendance | #78 Traditional information retrieval techniques use a histogram of keywords as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance. | ||
| other,13-2-P01-1007,ak | ] </term> can be parsed in <term> polynomial | time | </term> and many <term> classical grammatical | #1635 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. | |
| other,29-2-P01-1007,ak | increasing their <term> worst-case parsing | time | complexity </term> . For example , after <term> | #1652 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. | |
| other,22-3-P01-1007,ak | grammar </term> can be parsed in O ( n6 ) <term> | time | </term> . In this paper , we study a <term> | #1677 For example, after translation into an equivalent RCG, any tree adjoining grammar can be parsed in O(n6)time. | |
| It gives users the ability to spend their | time | finding more data relevant to their task | #3613 It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology. | ||
| performance of a <term> summarizer </term> , at | times | giving it a significant lead over <term> | #5420 It is found that the Bayesian approach generally leverages performance of a summarizer, at times giving it a significant lead over non-Bayesian models. | ||
| other,13-3-H05-1101,ak | reported , along with an <term> exponential | time | lower-bound </term> for certain classes of | #5734 Two hardness results for the class NP are reported, along with an exponential time lower-bound for certain classes of algorithms that are currently used in the literature. | |
| describing multi-event stories published over | time | . <term> Annotators </term> generated a list | #5766 We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. | ||
| series of workshops </term> . At the same | time | , the recent improvements in the <term> BLEU | #6402 At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences. | ||
| requiring significantly less development | time | . This is particularly important when building | #6789 Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMT gives competitive results to rule-based translation systems, requiring significantly less development time. | ||
| good <term> baseline results </term> in a short | time | . The tutorial will cover the basics of | #6852 Participants should be able, after attending this workshop, to set out building an SMT system themselves and achieving good baseline results in a short time. | ||
| other,6-2-P05-1032,ak | complexity </term> and <term> average retrieval | times | </term> for looking up <term> phrase translations | #8803 We detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure. | |
| other,10-3-P05-1032,ak | can be used to reduce the <term> retrieval | time | </term> by <term> orders of magnitude </term> | #8827 We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality. | |
| tech,3-6-P05-1067,ak | transducer </term> . We introduce a <term> polynomial | time | decoding algorithm </term> for the <term> model | #9882 We introduce a polynomial time decoding algorithm for the model. | |
| match with respect to <term> domain </term> and | time | is also important , and presents preliminary | #10468 This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time. | ||
| <term> domain </term> , <term> topic </term> and | time | . This paper presents the results of automatically | #10496 This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time. | ||
| other,8-5-E06-1004,ak | unlikely that there exists a <term> polynomial | time | solution </term> for any of these <term> hard | #10977 Since it is unlikely that there exists a polynomial time solution for any of these hard problems (unless P = NP and P#P = P), our results highlight and justify the need for developing polynomial time approximations for these computations. | |
| other,38-5-E06-1004,ak | the need for developing <term> polynomial | time | approximations </term> for these computations | #11007 Since it is unlikely that there exists a polynomial time solution for any of these hard problems (unless P = NP and P#P = P), our results highlight and justify the need for developing polynomial time approximations for these computations. | |
| other,7-4-E06-1031,ak | be exactly calculated in <term> quadratic | time | </term> . Furthermore , we will show how | #11320 Our measure can be exactly calculated in quadratic time. |
