Concordance

Query learning 30 (-46.6 per million)

other,12-2-H01-1042,bq	information about both the <term> human language	learning	process </term> , the <term> translation process	#595 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	of <term> MT output </term> . A <term> language	learning	experiment </term> showed that <term> assessors	#630 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
tech,28-4-H01-1055,bq	can be overcome by employing <term> machine	learning	techniques </term> . In this paper , we address	#1024 We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.
tech,3-2-P01-1008,bq	</term> . We present an <term> unsupervised	learning	algorithm </term> for <term> identification	#1781 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text.
tech,31-2-P01-1047,bq	resource sensitive logic </term> , and a <term>	learning	algorithm </term> from <term> structured data	#1977 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 alearning algorithm from structured data (based on a typing-algorithm and type-unification).
tech,5-1-P01-1070,bq	describe a set of <term> supervised machine	learning	</term> experiments centering on the construction	#2131 We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions.
tech,8-1-N03-1004,bq	<term> ensemble methods </term> in <term> machine	learning	</term> and other areas of <term> natural language	#2315 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.
tech,27-3-N03-1017,bq	relatively simple means : <term> heuristic	learning	</term> of <term> phrase translations </term>	#2616 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 translations from word-based alignments and lexical weighting of phrase translations.
	phrase translations </term> . Surprisingly ,	learning	<term> phrases </term> longer than three <term>	#2632 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance.
	</term> longer than three <term> words </term> and	learning	<term> phrases </term> from <term> high-accuracy	#2639 Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance.
	simple <term> unsupervised technique </term> for	learning	<term> morphology </term> by identifying <term>	#3160 We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton.
tech,18-3-P03-1002,bq	; and ( 2 ) <term> inductive decision tree	learning	</term> . The experimental results prove	#3771 It is based on: (1) an extended set of features; and (2) inductive decision tree learning.
tech,8-4-P03-1033,bq	automatically derived by <term> decision tree	learning	</term> using real <term> dialogue data </term>	#4361 Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system.
tech,4-1-P03-1050,bq	This paper presents an <term> unsupervised	learning	approach </term> to building a <term> non-English	#4435 This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer.
tech,19-1-P03-1058,bq	data </term> required for <term> supervised	learning	</term> . In this paper , we evaluate an	#4819 A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning.
tech,4-1-C04-1035,bq	difference . This paper presents a <term> machine	learning	</term> approach to bare <term> sluice disambiguation	#5154 This paper presents a machine learning approach to bare sluice disambiguation in dialogue.
tech,26-3-C04-1035,bq	</term> , and run two different <term> machine	learning	algorithms </term> : <term> SLIPPER </term> ,	#5209 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system.
tech,33-3-C04-1035,bq	: <term> SLIPPER </term> , a <term> rule-based	learning	algorithm </term> , and <term> TiMBL </term>	#5216 We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system.
tech,5-1-P04-2010,bq	This paper presents a novel <term> ensemble	learning	approach </term> to <term> resolving German	#7027 This paper presents a novel ensemble learning approach to resolving German pronouns.
tech,46-5-E06-1035,bq	top-level boundaries </term> , the <term> machine	learning	approach </term> that combines <term> lexical-cohesion	#10573 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task.


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