Concordance

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	At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English machine translation system </term><term> CCLINC ( Common Coalition Language System at Lincoln Laboratory ) </term> .	#392 At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory).
	We have built and will demonstrate an application of this approach called <term> LCS-Marine </term> .	#815 We have built and will demonstrate an application of this approach called LCS-Marine.
	We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this <term> technology </term> in <term> new domains </term> .	#888 We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this technology in new domains.
	Recent advances in <term> Automatic Speech Recognition technology </term> have put the goal of naturally sounding <term> dialog systems </term> within reach .	#918 Recent advances in Automatic Speech Recognition technologyhave put the goal of naturally sounding dialog systems within reach.
	<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#2022 Techniques for automatically training modules of a natural language generatorhave 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.
	Surprisingly , learning <term> phrases </term> longer than three <term> words </term> and learning <term> phrases </term> from <term> high-accuracy word-level alignment models </term> does not have a strong impact on performance .	#2648 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> FSM </term> provides two strategies for <term> language understanding </term> and have a high accuracy but little robustness and flexibility .	#3524 FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility.
	Experiment results have shown that a <term> system </term> that exploits the proposed <term> method </term> performs sufficiently and that holding multiple <term> candidates </term> for <term> understanding </term> results is effective .	#4256 Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective.
	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#4909 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged datahave in their sense coverage.
	The results show that the <term> features </term> in terms of which we formulate our <term> heuristic principles </term> have significant <term> predictive power </term> , and that <term> rules </term> that closely resemble our <term> Horn clauses </term> can be learnt automatically from these <term> features </term> .	#5255 The results show that the features in terms of which we formulate our heuristic principleshave significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.
	Along the way , we present the first comprehensive comparison of <term> unsupervised methods for part-of-speech tagging </term> , noting that published results to date have not been comparable across <term> corpora </term> or <term> lexicons </term> .	#5549 Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons.
	This paper investigates some <term> computational problems </term> associated with <term> probabilistic translation models </term> that have recently been adopted in the literature on <term> machine translation </term> .	#7446 This paper investigates some computational problems associated with probabilistic translation models that have recently been adopted in the literature on machine translation.
	This tends to support the view that despite recent speculative claims to the contrary , current <term> SMT models </term> do have limitations in comparison with dedicated <term> WSD models </term> , and that <term> SMT </term> should benefit from the better predictions made by the <term> WSD models </term> .	#7963 This tends to support the view that despite recent speculative claims to the contrary, current SMT models do have limitations in comparison with dedicated WSD models, and that SMT should benefit from the better predictions made by the WSD models.
	Over the last few years dramatic improvements have been made , and a number of comparative evaluations have shown , that <term> SMT </term> gives competitive results to <term> rule-based translation systems </term> , requiring significantly less development time .	#8012 Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMT gives competitive results to rule-based translation systems, requiring significantly less development time.
	Over the last few years dramatic improvements have been made , and a number of comparative evaluations have shown , that <term> SMT </term> gives competitive results to <term> rule-based translation systems </term> , requiring significantly less development time .	#8022 Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMT gives competitive results to rule-based translation systems, requiring significantly less development time.
	In this paper we study a set of problems that are of considerable importance to <term> Statistical Machine Translation ( SMT ) </term> but which have not been addressed satisfactorily by the <term> SMT research community </term> .	#9945 In this paper we study a set of problems that are of considerable importance to Statistical Machine Translation (SMT) but which have not been addressed satisfactorily by the SMT research community.
	Over the last decade , a variety of <term> SMT algorithms </term> have been built and empirically tested whereas little is known about the <term> computational complexity </term> of some of the fundamental problems of <term> SMT </term> .	#9966 Over the last decade, a variety of SMT algorithmshave been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT.
	We first apply approaches that have been proposed for <term> predicting top-level topic shifts </term> to the problem of <term> identifying subtopic boundaries </term> .	#10493 We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries.
	We also find that the <term> transcription errors </term> inevitable in <term> ASR output </term> have a negative impact on models that combine <term> lexical-cohesion and conversational features </term> , but do not change the general preference of approach for the two tasks .	#10619 We also find that the transcription errors inevitable in ASR outputhave a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
	Finally , we have shown that these results can be improved using a bigger and a more homogeneous <term> corpus </term> to train , that is , a bigger <term> corpus </term> written by one unique <term> author </term> .	#11285 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.


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