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translation ( MT ) systems </term> . We believe	that	these <term> evaluation techniques </term>	#583 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems.
with <term> intelligent mobile agents </term>	that	mediate between <term> users </term> and <term>	#806 We integrate a spoken language understanding system with intelligent mobile agentsthat mediate between users and information sources.
<term> language models ( LMs ) </term> . We find	that	simple <term> interpolation methods </term>	#1046 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle.
from <term> training data </term> . We show	that	the trained <term> SPR </term> learns to select	#1435 We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.
two distinct <term> datasets </term> , we find	that	<term> indexing </term> according to simple	#1538 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models.
<term> queries </term> containing them . I show	that	the <term> performance </term> of a <term> search	#1873 I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics.
</term> of <term> Minimalist grammars </term> ,	that	are <term> Stabler 's formalization </term>	#1935 We provide a logical definition of Minimalist grammars, that are Stabler's formalization of Chomsky's minimalist program.
baseline sentence planners </term> . We show	that	the <term> trainable sentence planner </term>	#2101 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.
for <term> utterance classification </term>	that	does not require <term> manual transcription	#2213 This paper describes a method for utterance classificationthat does not require manual transcription of training data.
multi-level answer resolution algorithm </term>	that	combines results from the <term> answering	#2379 We present our multi-level answer resolution algorithmthat combines results from the answering agents at the question, passage, and/or answer levels.
<term> annotation experiment </term> and showed	that	<term> human annotators </term> can reliably	#2486 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses.
</term> and <term> decoding algorithm </term>	that	enables us to evaluate and compare several	#2549 We propose a new phrase-based translation model and decoding algorithmthat enables us to evaluate and compare several, previously proposed phrase-based translation models.
character recognition ( OCR ) model </term>	that	describes an end-to-end process in the <term>	#2683 In this paper, we introduce a generative probabilistic optical character recognition (OCR) modelthat describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
a new <term> part-of-speech tagger </term>	that	demonstrates the following ideas : ( i	#2915 We present a new part-of-speech taggerthat 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.
target <term> recognition task </term> , but also	that	it is possible to get bigger performance	#3060 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> algorithms </term> . The results show	that	it can provide a significant improvement	#3274 The results show that it can provide a significant improvement in alignment quality.
or <term> pronoun </term><term> seeds </term>	that	correspond to the <term> concept </term> for	#3316 This approach only requires a few common noun or pronoun seedsthat correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
<term> statistical machine translation </term>	that	uses a much simpler set of <term> model parameters	#3403 In this paper, we describe a phrase-based unigram model for statistical machine translationthat uses a much simpler set of model parameters than similar phrase-based models.
novel , customizable <term> IE paradigm </term>	that	takes advantage of <term> predicate-argument	#3722 In this paper we present a novel, customizable IE paradigmthat takes advantage of predicate-argument structures.
The results of the experiments demonstrate	that	the <term> HDAG Kernel </term> is superior	#3870 The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.


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