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probabilistic context-free grammars </term> working in	a	' synchronous ' way . Two <term> hardness	#7470 These models can be viewed as pairs of probabilistic context-free grammars working in a 'synchronous' way.
Generation of Referring Expressions </term> : (	a	) <term> numeric-valued attributes </term>	#10661 This paper discusses two problems that arise in the Generation of Referring Expressions: ( a) numeric-valued attributes, such as size or location; (b) perspective-taking in reference.
</term> achieved 89.75 % <term> F-measure </term> ,	a	13 % relative decrease in <term> F-measure	#8843 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%.
automatically acquiring new <term> stems </term> from	a	155 million <term> word </term><term> unsegmented	#4723 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus.
features </term> . The best system obtains	a	18.6 % improvement over the <term> baseline	#9640 The best system obtains a 18.6% improvement over the baseline on a standard Arabic-English translation task.
questions correctly answered </term> , and	a	32.8 % improvement according to the <term>	#2424 Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
<term> answer resolution algorithm </term> show	a	35.0 % relative improvement over our <term>	#2406 Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
accuracy </term> rate from 60 % to 75 % ,	a	37 % reduction in error . We discuss <term>	#19045 In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.
<term> error rate </term> dropped to 4.1 % ---	a	45 % reduction in <term> error </term> compared	#17206 Using only 40 utterances from the target speaker for adaptation, the error rate dropped to 4.1% --- a 45% reduction in error compared to the SI result.
</term> . The models were constructed using	a	5K <term> vocabulary </term> and trained using	#21247 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus.
recognition system </term> , experiments show	a	7 % improvement in <term> recognition accuracy	#21271 Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model.
<term> SI recognition </term> , we achieved	a	7.5 % <term> word error rate </term> on a standard	#17081 With only 12 training speakers for SI recognition, we achieved a 7.5% word error rate on a standard grammar and test set from the DARPA Resource Management corpus.
<term> vocabulary </term> and trained using	a	76 million <term> word </term><term> Wall Street	#21253 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus.
, the resulting <term> tagger </term> gives	a	97.24 % <term> accuracy </term> on the <term>	#2988 Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.
</term> . The <term> model </term> is based on	a	<term> balance matching operation </term> for	#19820 The model is based on a balance matching operation for two lists of the feature sets, which provides four effects: the reduction of analysis cost, the improvement of word disambiguation, the interpretation of ellipses, and robust analysis.
combined the <term> log-likelihood </term> under	a	<term> baseline model </term> ( that of <term>	#8805 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model.
as either coherent or incoherent ( given	a	<term> baseline </term> of 54.55 % ) . We propose	#2532 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%).
the system yields higher performance than	a	<term> baseline </term> on all three aspects	#6747 Results indicate that the system yields higher performance than a baseline on all three aspects.
contains a <term> recognition network </term> ,	a	<term> basic mapping </term> , additional <term>	#12484 Each generalized metaphor contains a recognition network, a basic mapping, additional transfer mappings, and an implicit intention component.
semantic constraints </term> and thus provide	a	basis for a useful <term> disambiguation	#16714 The results of the experiment show that in most of the cases the cooccurrence statistics indeed reflect the semantic constraints and thus provide a basis for a useful disambiguation tool.


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