| encouraging results . The paper presents a | method | for <term> word sense disambiguation </term> | #6440 The paper presents a method for word sense disambiguation based on parallel corpora. | |
| based on <term> parallel corpora </term> . The | method | exploits recent advances in <term> word alignment | #6451 The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. | |
| <term> WSD system </term> , implementing the | method | described herein showed very encouraging | #6508 The evaluation of the WSD system, implementing the method described herein showed very encouraging results. | |
| </term> and <term> Text Summarisation </term> . Our | method | takes advantage of the different way in | #6959 Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web. | |
| pronouns </term> . <term> Boosting </term> , the | method | in question , combines the moderately accurate | #7037 Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. | |
| tech,4-1-H05-1095,bq | phrase-based statistical machine translation | method | </term> , based on <term> non-contiguous phrases | #7345 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. |
| tech,1-2-H05-1095,bq | i.e. <term> phrases </term> with gaps . A <term> | method | </term> for producing such <term> phrases </term> | #7358 Amethod for producing such phrases from a word-aligned corpora is proposed. |
| tech,16-3-H05-1095,bq | phrases </term> , as well as a <term> training | method | </term> based on the maximization of <term> | #7387 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. |
| tech,10-5-H05-1095,bq | that demonstrate how the proposed <term> | method | </term> allows to better generalize from | #7424 Experimental results are presented, that demonstrate how the proposedmethod allows to better generalize from the training data. |
| tech,3-1-H05-2007,bq | <term> metrics </term> . We describe a <term> | method | </term> for identifying systematic <term> patterns | #7631 We describe amethod for identifying systematic patterns in translation data using part-of-speech tag sequences. |
| tech,5-3-I05-5003,bq | also introduce a novel <term> classification | method | </term> based on <term> PER </term> which leverages | #8373 We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
| tech,3-1-I05-5008,bq | in the experiments . We propose a <term> | method | </term> that automatically generates <term> | #8444 We propose amethod that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. |
| tech,6-3-I05-5008,bq | paraphrase </term> sets produced by this <term> | method | </term> thus seem adequate as <term> reference | #8551 The paraphrase sets produced by thismethod thus seem adequate as reference sets to be used for MT evaluation. |
| tech,4-5-J05-1003,bq | </term> into account . We introduce a new <term> | method | </term> for the <term> reranking task </term> | #8763 We introduce a newmethod for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
| tech,3-6-J05-1003,bq | 1998 ) </term> . We apply the <term> boosting | method | </term> to <term> parsing </term> the <term> Wall | #8790 We apply the boosting method to parsing the Wall Street Journal treebank. |
| tech,1-7-J05-1003,bq | Street Journal treebank </term> . The <term> | method | </term> combined the <term> log-likelihood </term> | #8800 Themethod 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. |
| boosting approach </term> . We argue that the | method | is an appealing alternative — in terms | #8909 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within log-linear (maximum-entropy) models. | |
| tech,4-1-J05-4003,bq | generation </term> . We present a novel <term> | method | </term> for <term> discovering parallel sentences | #8987 We present a novelmethod for discovering parallel sentences in comparable, non-parallel corpora. |
| <term> non-parallel corpus </term> . Thus , our | method | can be applied with great benefit to <term> | #9103 Thus, our method can be applied with great benefit to language pairs for which only scarce resources are available. | |
| tech,1-2-P05-1034,bq | <term> phrasal translation </term> . This <term> | method | </term> requires a <term> source-language </term> | #9226 Thismethod requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. |
