| pharmaceutical news archive </term> as part of | their | industry watch function . We also report | #339 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function. | ||
| factors on which the <term> assessors </term> made | their | decisions . We tested this to see if similar | #659 Even more illuminating was the factors on which the assessors made their decisions. | ||
| minutes per extract to determine whether | they | believed the sample output to be an <term> | #723 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation. | ||
| machine translation </term> . Additionally , | they | were asked to mark the <term> word </term> | #741 Additionally, they were asked to mark the word at which they made this decision. | ||
| asked to mark the <term> word </term> at which | they | made this decision . The results of this | #750 Additionally, they were asked to mark the word at which they made this decision. | ||
| </term> , tactical personnel can converse with | their | logistics system to place a <term> supply | #836 Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request. | ||
| instruct the <term> system </term> to notify | them | when the status of a <term> request </term> | #872 Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete. | ||
| understand more of what the <term> user </term> tells | them | , they need to be more sophisticated at | #954 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. | ||
| of what the <term> user </term> tells them , | they | need to be more sophisticated at responding | #956 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. | ||
| </term> and the decision of how to combine | them | into one or more <term> sentences </term> . | #1328 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. | ||
| <term> word N-gram models </term> . Further , in | their | optimum configuration , <term> bag-of-words | #1562 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. | ||
| equivalent <term> RCGs </term> without increasing | their | <term> worst-case parsing time complexity | #1649 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. | ||
| poorly on <term> queries </term> containing | them | . I show that the <term> performance </term> | #1870 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. | ||
| recognition hypotheses ( SRH ) </term> in terms of | their | <term> semantic coherence </term> . We conducted | #2476 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence. | ||
| data . It gives users the ability to spend | their | time finding more data relevant to their | #3612 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. | ||
| their time finding more data relevant to | their | task , and gives them translingual reach | #3619 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. | ||
| data relevant to their task , and gives | them | translingual reach into other <term> languages | #3624 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. | ||
| several levels of both <term> chunks </term> and | their | <term> relations </term> , and then efficiently | #3819 The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs. | ||
| manually sense-tagged data </term> have in | their | sense coverage . Our analysis also highlights | #4913 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. | ||
| </term> , are now well established . Yet , | they | are scarcely used for the assessment of | #6245 Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of the word segmentation problem. |
