Concordance

Query C04-1080 15 (699.3 per million)

tech,4-1-C04-1080,bq	effect on both tasks . We present a new <term>	HMM tagger	</term> that exploits <term> context </term>	#5500 We present a newHMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case.
other,8-1-C04-1080,bq	<term> HMM tagger </term> that exploits <term>	context	</term> on both sides of a <term> word </term>	#5504 We present a new HMM tagger that exploitscontext on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case.
other,14-1-C04-1080,bq	<term> context </term> on both sides of a <term>	word	</term> to be tagged , and evaluate it in	#5510 We present a new HMM tagger that exploits context on both sides of aword to be tagged, and evaluate it in both the unsupervised and supervised case.
other,25-1-C04-1080,bq	tagged , and evaluate it in both the <term>	unsupervised and supervised case	</term> . Along the way , we present the	#5521 We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both theunsupervised and supervised case.
tech,11-2-C04-1080,bq	first comprehensive comparison of <term>	unsupervised methods for part-of-speech tagging	</term> , noting that published results to	#5537 Along the way, we present the first comprehensive comparison ofunsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons.
lr,28-2-C04-1080,bq	date have not been comparable across <term>	corpora	</term> or <term> lexicons </term> . Observing	#5554 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 acrosscorpora or lexicons.
lr,30-2-C04-1080,bq	comparable across <term> corpora </term> or <term>	lexicons	</term> . Observing that the quality of the	#5556 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 orlexicons.
lr,6-3-C04-1080,bq	Observing that the quality of the <term>	lexicon	</term> greatly impacts the <term> accuracy	#5564 Observing that the quality of thelexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable.
measure(ment),10-3-C04-1080,bq	<term> lexicon </term> greatly impacts the <term>	accuracy	</term> that can be achieved by the <term>	#5568 Observing that the quality of the lexicon greatly impacts theaccuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable.
tech,17-3-C04-1080,bq	</term> that can be achieved by the <term>	algorithms	</term> , we present a method of <term> HMM	#5575 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by thealgorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable.
tech,24-3-C04-1080,bq	algorithms </term> , we present a method of <term>	HMM training	</term> that improves <term> accuracy </term>	#5582 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method ofHMM training that improves accuracy when training of lexical probabilities is unstable.
measure(ment),28-3-C04-1080,bq	<term> HMM training </term> that improves <term>	accuracy	</term> when training of <term> lexical probabilities	#5586 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improvesaccuracy when training of lexical probabilities is unstable.
other,32-3-C04-1080,bq	<term> accuracy </term> when training of <term>	lexical probabilities	</term> is unstable . Finally , we show how	#5590 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training oflexical probabilities is unstable.
tech,7-4-C04-1080,bq	unstable . Finally , we show how this new <term>	tagger	</term> achieves state-of-the-art results	#5602 Finally, we show how this newtagger achieves state-of-the-art results in a supervised, non-training intensive framework.
tech,13-4-C04-1080,bq	achieves state-of-the-art results in a <term>	supervised , non-training intensive framework	</term> . Past work of <term> generating referring	#5608 Finally, we show how this new tagger achieves state-of-the-art results in asupervised , non-training intensive framework.


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