Concordance

Query language 108 >
Negative filter H01-1041 104 (4,848.7 per million)

other,22-1-H01-1042,bq	the <term> evaluation </term> of <term> human	language	learners </term> , to the <term> output </term>	#567 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems.
other,12-2-H01-1042,bq	provide information about both the <term> human	language	learning process </term> , the <term> translation	#594 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.
other,1-4-H01-1042,bq	</term> of <term> MT output </term> . A <term>	language	learning experiment </term> showed that <term>	#629 Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
other,9-4-H01-1042,bq	differentiate <term> native from non-native	language	essays </term> in less than 100 <term> words	#640 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
tech,3-2-H01-1049,bq	sources </term> . We integrate a <term> spoken	language	understanding system </term> with <term> intelligent	#799 We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources.
other,13-3-H01-1055,bq	extensively studied by the <term> natural	language	generation community </term> , though rarely	#982 The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems.
model,11-1-H01-1058,bq	address the problem of combining several <term>	language	models ( LMs ) </term> . We find that simple	#1038 In this paper, we address the problem of combining severallanguage models (LMs).
tech,11-5-H01-1058,bq	clearly show the need for a <term> dynamic	language	model combination </term> to improve the <term>	#1143 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further.
tech,17-1-H01-1070,bq	key prediction </term> and <term> Thai-English	language	identification </term> . The paper also proposes	#1259 This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification.
tech,10-3-H01-1070,bq	than 99 % <term> accuracy </term> in both <term>	language	identification </term> and <term> key prediction	#1287 Our algorithm reported more than 99% accuracy in bothlanguage identification and key prediction.
other,10-5-P01-1007,bq	of the <term> main parser </term> for a <term>	language	L </term> are directed by a <term> guide </term>	#1710 The non-deterministic parsing choices of the main parser for alanguage L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L.
tech,6-1-P01-1008,bq	interpretation and generation of natural	language	</term> , current systems use manual or semi-automatic	#1764 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases.
tech,14-2-P01-1009,bq	attention </term> , yet present <term> natural	language	search engines </term> perform poorly on <term>	#1861 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them.
tech,12-4-P01-1009,bq	operational semantics </term> of <term> natural	language	applications </term> improve , even larger	#1916 The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible.
tech,7-1-P01-1056,bq	training </term> modules of a <term> natural	language	generator </term> have recently been proposed	#2020 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
other,11-4-N03-1001,bq	evaluated on three different <term> spoken	language	system domains </term> . Motivated by the	#2302 The classification accuracy of the method is evaluated on three different spoken language system domains.
tech,14-1-N03-1004,bq	learning </term> and other areas of <term> natural	language	processing </term> , we developed a <term>	#2321 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.
other,9-3-N03-1017,bq	results , which hold for all examined <term>	language	pairs </term> , suggest that the highest	#2597 Our empirical results, which hold for all examinedlanguage pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations.
tech,6-1-N03-2003,bq	<term> training data </term> suitable for <term>	language	modeling </term> of <term> conversational speech	#3020 Sources of training data suitable forlanguage modeling of conversational speech are limited.
model,28-1-N03-2006,bq	corpus </term> and , in addition , the <term>	language	model </term> of an in-domain <term> monolingual	#3107 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, thelanguage model of an in-domain monolingual corpus.


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