| involved in the decision making process will be | presented | here . <term> Listen-Communicate-Show ( LCS | #777 The results of this experiment, along with a preliminary analysis of the factors involved in the decision making process will be presented here. | ||
| <term> sentences </term> . In this paper , we | present | <term> SPoT </term> , a <term> sentence planner | #1340 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. | ||
| </term> to collect <term> paraphrases </term> . We | present | an <term> unsupervised learning algorithm | #1779 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. | ||
| syntactic paraphrases </term> . This paper | presents | a formal analysis for a large class of <term> | #1818 This paper presents a formal analysis for a large class of words called alternative markers, which includes other (than), such (as), and besides. | ||
| </term> to warrant serious attention , yet | present | <term> natural language search engines </term> | #1860 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. | ||
| other adopting statistical techniques . We | present | our <term> multi-level answer resolution | #2374 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. | ||
| precision metric </term> . In this paper we | present | <term> ONTOSCORE </term> , a system for scoring | #2440 In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. | ||
| more useful for <term> NLP tasks </term> . We | present | an implementation of the <term> model </term> | #2746 We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text. | ||
| </term> from printed <term> text </term> . We | present | an application of <term> ambiguity packing | #2786 We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation. | ||
| constraint-based parser/generator </term> . We | present | a new <term> part-of-speech tagger </term> | #2911 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. | ||
| more complex mixtures of techniques . We | present | a <term> syntax-based constraint </term> for | #3230 We present a syntax-based constraint for word alignment, known as the cohesion constraint. | ||
| </term> and <term> successive learners </term> is | presented | . This approach only requires a few common | #3304 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. | ||
| answering session </term> . In this paper we | present | a novel , customizable <term> IE paradigm | #3716 In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. | ||
| <term> NP - and non-NP-antecedents </term> . We | present | a set of <term> features </term> designed for | #4000 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. | ||
| for <term> skilled users </term> . This paper | presents | an <term> unsupervised learning approach </term> | #4434 This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. | ||
| feedback </term> . Based on these results , we | present | an <term> ECA </term> that uses <term> verbal | #5095 Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state. | ||
| for <term> LTAG </term> and <term> HPSG </term> is | presented | . We demonstrate that an approximation | #5122 An empirical comparison of CFG filtering techniques for LTAG and HPSG is presented. | ||
| on <term> noun phrases </term> . This paper | presents | a <term> maximum entropy word alignment algorithm | #5277 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data. | ||
| annotation performance </term> . The paper | presents | a <term> Bayesian model </term> for <term> text | #5365 The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. | ||
| than <term> parse trees </term> . This paper | presents | a <term> phrase-based statistical machine | #5583 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. |
