Concordance

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improve upon this initial <term> ranking </term> ,	using	additional <term> features </term> of the <term>	#8701 A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
<term> phrases </term> while simultaneously	using	less <term> memory </term> than is required	#9146 In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations.
efficient <term> decoder </term> and show that	using	these <term> tree-based models </term> in combination	#9281 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
outputs </term> of our <term> MT system </term>	using	the <term> NIST and Bleu automatic MT evaluation	#9517 We evaluate the outputs of our MT systemusing the NIST and Bleu automatic MT evaluation software.
</term> . We show that this task can be done	using	<term> bilingual parallel corpora </term> ,	#9675 We show that this task can be done using bilingual parallel corpora, a much more commonly available resource.
one <term> language </term> can be identified	using	a <term> phrase </term> in another language	#9706 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.
bilingual parallel corpus </term> to be ranked	using	<term> translation probabilities </term> ,	#9733 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.
paraphrase extractio and ranking methods </term>	using	a set of <term> manual word alignments </term>	#9759 We evaluate our paraphrase extractio and ranking methodsusing a set of manual word alignments, and contrast the quality with paraphrases extracted from automatic alignments.
sense per collocation observation </term> by	using	triplets of <term> words </term> instead of	#10158 This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs.
<term> two-step clustering process </term>	using	<term> sentence co-occurrences </term> as <term>	#10173 The combination with a two-step clustering processusing sentence co-occurrences as features allows for accurate results.
four-participants face-to-face meetings </term>	using	<term> Bayesian Network </term> and <term> Naive	#10245 We present results on addressee identification in four-participants face-to-face meetingsusing Bayesian Network and Naive Bayes classifiers.
the impact on <term> performance </term> of	using	<term> ASR output </term> as opposed to <term>	#10517 We then explore the impact on performance of using ASR output as opposed to human transcription.
a <term> token classification task </term> ,	using	various <term> tagging strategies </term> to	#10813 There are several approaches that model information extraction as a token classification task, using various tagging strategies to combine multiple tokens.
automatically from <term> raw text </term> . Experiments	using	the <term> SemCor </term> and <term> Senseval-3	#11025 Experiments using the SemCor and Senseval-3 data sets demonstrate that our ensembles yield significantly better results when compared with state-of-the-art.
In this paper , we describe the research	using	<term> machine learning techniques </term>	#11208 In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque.
shown that these results can be improved	using	a bigger and a more homogeneous <term> corpus	#11293 Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.
</term> and <term> linguistic pattern </term> . By	using	them , we can automatically extract such	#11442 By using them, we can automatically extract such sentences that express opinion.
Path-based inference rules </term> may be written	using	a <term> binary relational calculus notation	#12099 Path-based inference rules may be written using a binary relational calculus notation.
constructed in a <term> semantic network </term>	using	a variant of a <term> predicate calculus	#12139 Node-based inference rules can be constructed in a semantic networkusing a variant of a predicate calculus notation.
the sum of each <term> character </term> . By	using	commands or <term> rules </term> which are	#12312 By using commands or rules which are defined to facilitate the construction of format expected or some mathematical expressions, elaborate and pretty documents can be successfully obtained.


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