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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.
differs from that of Pereira and Shieber by	using	a <term> logical model </term> in place of	#14761 Our interpretation differs from that of Pereira and Shieber by using a logical model in place of a denotational semantics.
elicited from duplicating the experiment	using	<term> machine translation output </term> .	#677 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output.
derived by <term> decision tree learning </term>	using	real <term> dialogue data </term> collected	#4362 Moreover, the models are automatically derived by decision tree learningusing real dialogue data collected by the system.
proprietary <term> Arabic stemmer </term> built	using	<term> rules </term> , <term> affix lists </term>	#4551 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.
sense disambiguation performance </term> ,	using	standard <term> WSD evaluation methodology	#7814 We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task.
NIST score </term> demonstrated the effect of	using	an out-of-domain <term> bilingual corpus </term>	#3139 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model.
<term> Named Entity ( NE ) tagging </term>	using	<term> concept-based seeds </term> and <term>	#3296 A novel bootstrapping approach to Named Entity (NE) taggingusing concept-based seeds and successive learners is presented.
</term> in <term> unannotated text </term> by	using	a fully automatic sequence of <term> preprocessing	#7083 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process.
</term> based on the results . The evaluation	using	another 23 subjects showed that the proposed	#5713 The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions.
</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.
performance gains from the <term> data </term> by	using	<term> class-dependent interpolation </term>	#3073 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.
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.
<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.
<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.
Sentence ambiguities </term> can be resolved by	using	domain targeted preference knowledge without	#16325 Sentence ambiguities can be resolved by using domain targeted preference knowledge without using complicated large knowledgebases.
( <term> anaphora </term> ) . This method of	using	<term> expectations </term> to aid the understanding	#13102 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
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.
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.
<term> word string </term> has been obtained by	using	a different <term> LM </term> . Actually ,	#1110 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM.


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