| documentation . The question is , however , | how | an interesting information piece would | #42 The question is, however, how an interesting information piece would be found in a large database. | ||
| for this purpose . In this paper we show | how | two standard outputs from <term> information | #279 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser. | ||
| standard <term> text browser </term> . We describe | how | this information is used in a <term> prototype | #315 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. | ||
| context of <term> dialog systems </term> . We show | how | research in <term> generation </term> can be | #997 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques. | ||
| adapted to <term> dialog systems </term> , and | how | the high cost of hand-crafting <term> knowledge-based | #1009 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques. | ||
| elementary speech acts </term> and the decision of | how | to combine them into one or more <term> sentences | #1325 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> are limited . In this paper , we show | how | <term> training data </term> can be supplemented | #3035 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. | ||
| </term> . The demonstration will focus on | how | <term> JAVELIN </term> processes <term> questions | #3668 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus. | ||
| information in English . We demonstrate | how | <term> errors </term> in the <term> machine translations | #5248 We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases. | ||
| explicitly encodes and exploits information on | how | <term> human judgments </term> are distributed | #5380 The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. | ||
| results are presented , that demonstrate | how | the proposed method allows to better generalize | #5665 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from the training data. | ||
| questions </term> are time-sensitive ( e.g . | How | many victims have been found ? ) Judges | #5799 Because of the dynamic nature of the stories, many questions are time-sensitive (e.g. How many victims have been found?) | ||
| translation systems </term> , and demonstrate | how | our application can be used by developers | #6063 We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output. | ||
| <term> features </term> , without concerns about | how | these <term> features </term> interact or overlap | #8099 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 these features into account. | ||
| array-based data structure </term> . We show | how | sampling can be used to reduce the <term> | #8818 We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality. | ||
| <term> chunking </term> . We also demonstrate | how | <term> semantic information </term> such as | #9356 We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. | ||
| </term> between <term> arguments </term> . We show | how | to build a joint <term> model </term> of <term> | #10090 We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log-linear models. | ||
| statistical machine translation </term> , we show | how | <term> paraphrases </term> in one language | #10187 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. | ||
| translation probabilities </term> , and show | how | it can be refined to take <term> contextual | #10228 We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual information into account. | ||
| classifiers </term> . First , we investigate | how | well the <term> addressee </term> of a <term> | #11194 First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. |
