| tech,4-2-H01-1070,ak | paper also proposes <term> rule-reduction | algorithm | </term> applying <term> mutual information </term> | #1267 The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. | |
| tech,1-3-H01-1070,ak | error-correction rules </term> . Our <term> | algorithm | </term> reported more than 99 % <term> accuracy | #1278 Ouralgorithm reported more than 99% accuracy in both language identification and key prediction. | |
| tech,3-2-P01-1008,ak | We present an <term> unsupervised learning | algorithm | </term> for <term> identification of paraphrases | #1783 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,ak | sensitive logic </term> , and a <term> learning | algorithm | </term> from <term> structured data </term> ( | #1979 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 a learning algorithm from structured data (based on a typing-algorithm and type-unification). | |
| tech,3-3-N03-1004,ak | present our <term> multi-level answer resolution | algorithm | </term> that combines results from the <term> | #2379 We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. | |
| tech,6-4-N03-1004,ak | effectiveness of our <term> answer resolution | algorithm | </term> show a 35.0 % <term> relative improvement | #2405 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. | |
| tech,8-1-N03-1017,ak | translation model </term> and <term> decoding | algorithm | </term> that enables us to evaluate and compare | #2549 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. | |
| tech,10-3-N03-2017,ak | <term> constraint </term> in two different <term> | algorithms | </term> . The results show that it can provide | #3270 We evaluate the utility of this constraint in two differentalgorithms. | |
| tech,17-2-P03-1051,ak | uses it to bootstrap an <term> unsupervised | algorithm | </term> to build the <term> Arabic word segmenter | #4658 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. | |
| tech,1-3-P03-1051,ak | unsegmented Arabic corpus </term> . The <term> | algorithm | </term> uses a <term> trigram language model | #4673 Thealgorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. | |
| tech,9-5-P03-1051,ak | accuracy </term> , we use an <term> unsupervised | algorithm | </term> for automatically acquiring new <term> | #4718 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. | |
| tech,9-7-P03-1051,ak | state-of-the-art performance and the <term> | algorithm | </term> can be used for many <term> highly | #4776 We believe this is a state-of-the-art performance and thealgorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. | |
| tech,4-1-H05-1012,ak | presents a <term> maximum entropy word alignment | algorithm | </term> for <term> Arabic-English </term> based | #5283 This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data. | |
| tech,3-5-H05-1012,ak | translation tests </term> . Performance of the <term> | algorithm | </term> is contrasted with <term> human annotation | #5355 Performance of thealgorithm is contrasted with human annotation performance. | |
| tech,20-3-H05-1101,ak | lower-bound </term> for certain classes of <term> | algorithms | </term> that are currently used in the literature | #5740 Two hardness results for the class NP are reported, along with an exponential time lower-bound for certain classes ofalgorithms that are currently used in the literature. | |
| tech,6-9-J05-1003,ak | The article also introduces a new <term> | algorithm | </term> for the <term> boosting approach </term> | #8232 The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| tech,8-10-J05-1003,ak | significant efficiency gains for the new <term> | algorithm | </term> over the obvious implementation of | #8260 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. | |
| tech,3-6-P05-1067,ak | introduce a <term> polynomial time decoding | algorithm | </term> for the <term> model </term> . We evaluate | #9884 We introduce a polynomial time decoding algorithm for the model. | |
| tech,1-4-P05-1069,ak | bigram features </term> . Our <term> training | algorithm | </term> can easily handle millions of <term> | #10008 Our training algorithm can easily handle millions of features. | |
| tech,8-2-E06-1004,ak | the last decade , a variety of <term> SMT | algorithms | </term> have been built and empirically tested | #10902 Over the last decade, a variety of SMT algorithms have been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT. |
