Concordance

Query from 104 (4,848.7 per million)

	this paper we show how two standard outputs	from	<term> information extraction ( IE ) systems	#283 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser.
other,9-4-H01-1042,bq	assessors </term> can differentiate <term> native	from	non-native language essays </term> in less	#638 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
	see if similar criteria could be elicited	from	duplicating the experiment using <term> machine	#673 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output.
	<term> word or semantic error rate </term> )	from	a list of <term> word strings </term> , where	#1095 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.
	ranking rules </term> automatically learned	from	<term> training data </term> . We show that	#1429 The SPR uses ranking rules automatically learned from training data.
	<term> identification of paraphrases </term>	from	a <term> corpus of multiple English translations	#1787 We present an unsupervised learning algorithm for identification of paraphrasesfrom a corpus of multiple English translations of the same source text.
	</term> , and a <term> learning algorithm </term>	from	<term> structured data </term> ( based on a	#1979 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithmfrom structured data (based on a typing-algorithm and type-unification).
	These <term> models </term> , which are built	from	<term> shallow linguistic features </term>	#2149 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals.
	</term> which is based on combining the results	from	different <term> answering agents </term> searching	#2341 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 answers in multiple corpora.
	resolution algorithm </term> that combines results	from	the <term> answering agents </term> at the <term>	#2382 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels.
	</term> of <term> phrase translations </term>	from	<term> word-based alignments </term> and <term>	#2620 Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translationsfrom word-based alignments and lexical weighting of phrase translations.
	words </term> and learning <term> phrases </term>	from	<term> high-accuracy word-level alignment	#2641 Surprisingly, learning phrases longer than three words and learning phrasesfrom high-accuracy word-level alignment models does not have a strong impact on performance.
	noisy channel framework </term> , progressing	from	generation of <term> true text </term> through	#2695 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.
	</term> of <term> translation lexicons </term>	from	<term> printed text </term> . We present an	#2780 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 lexiconsfrom printed text.
	can be supplemented with <term> text </term>	from	the <term> web </term> filtered to match the	#3042 In this paper, we show how training data can be supplemented with textfrom 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.
	possible to get bigger performance gains	from	the <term> data </term> by using <term> class-dependent	#3069 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> , the <term> blocks </term> are learned	from	<term> source interval projections </term>	#3451 During training, the blocks are learned from source interval projections using an underlying word alignment.
	topical report </term> , culling information	from	a large inflow of <term> multilingual , multimedia	#3594 The TAP-XL Automated Analyst's Assistant is an application designed to help an English-speaking analyst write a topical report, culling information from a large inflow of multilingual, multimedia data.
	most likely <term> answer candidates </term>	from	the given <term> text corpus </term> . The	#3678 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidatesfrom the given text corpus.
	inducing <term> semantic verb classes </term>	from	undisambiguated <term> corpus data </term>	#3897 Previous research has demonstrated the utility of clustering in inducing semantic verb classesfrom undisambiguated corpus data.


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