Concordance

Query in 338 (15,758.3 per million)

concatenation languages [ RCL ] </term> can be parsed	in	<term> polynomial time </term> and many classical	#1633 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.
tree adjoining grammar </term> can be parsed	in	<term> O ( n6 ) time </term> . In this paper	#1672 For example, after translation into an equivalent RCG, any tree adjoining grammar can be parsed in O(n6) time.
<term> words </term> appear frequently enough	in	<term> dialog </term> to warrant serious <term>	#1851 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them.
</term> ) , a <term> parsing-as-deduction </term>	in	a <term> resource sensitive logic </term> ,	#1969 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deductionin a resource sensitive logic, and a learning algorithm from structured data (based on a typing-algorithm and type-unification).
success of <term> ensemble methods </term>	in	<term> machine learning </term> and other areas	#2313 Motivated by the success of ensemble methodsin machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora.
</term> searching for <term> answers </term>	in	multiple <term> corpora </term> . The <term>	#2348 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answersin multiple corpora.
improvement over our <term> baseline system </term>	in	the number of <term> questions correctly	#2415 Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline systemin the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
speech recognition hypotheses ( SRH ) </term>	in	terms of their <term> semantic coherence </term>	#2472 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH)in terms of their semantic coherence.
that , it successfully classifies 73.2 %	in	a <term> German corpus </term> of 2.284 <term>	#2518 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
</term> that describes an end-to-end process	in	the <term> noisy channel framework </term>	#2688 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that 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.
The <term> model </term> is designed for use	in	<term> error correction </term> , with a focus	#2718 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks.
</term> of black-box <term> OCR systems </term>	in	order to make it more useful for <term> NLP	#2733 The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systemsin order to make it more useful for NLP tasks.
iii ) effective use of <term> priors </term>	in	<term> conditional loglinear models </term>	#2963 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 priorsin conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
out-of-domain <term> bilingual corpus </term> and ,	in	addition , the <term> language model </term>	#3103 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus.
morphology </term> by identifying <term> hubs </term>	in	an <term> automaton </term> . For our purposes	#3165 We describe a simple unsupervised technique for learning morphology by identifying hubsin an automaton.
, a <term> hub </term> is a <term> node </term>	in	a <term> graph </term> with <term> in-degree </term>	#3178 For our purposes, a hub is a nodein a graph with in-degree greater than one and out-degree greater than one.
to be mapped to non-overlapping intervals	in	the <term> French sentence </term> . We evaluate	#3254 It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence.
the utility of this <term> constraint </term>	in	two different <term> algorithms </term> . The	#3266 We evaluate the utility of this constraintin two different algorithms.
it can provide a significant improvement	in	<term> alignment quality </term> . A novel <term>	#3281 The results show that it can provide a significant improvement in alignment quality.
<term> natural language understanding </term>	in	a <term> dialogue system </term> . We build	#3491 In this paper, we propose a novel Cooperative Model for natural language understandingin a dialogue system.


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