| lr,12-2-P01-1008,ak | identification of paraphrases </term> from a <term> | corpus | of multiple English translations </term> | #1790 We present an unsupervised learning algorithm for identification of paraphrases from acorpus of multiple English translations of the same source text. | |
| lr,44-1-N03-1004,ak | for <term> answers </term> in multiple <term> | corpora | </term> . The <term> answering agents </term> | #2351 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiplecorpora. | |
| lr,19-4-N03-1012,ak | successfully classifies 73.2 % in a <term> German | corpus | </term> of 2.284 <term> SRHs </term> as either | #2522 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%). | |
| lr,12-1-N03-2006,ak | </term> based on a <term> small-sized bilingual | corpus | </term> , we use an <term> out-of-domain bilingual | #3094 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. | |
| lr,19-1-N03-2006,ak | , we use an <term> out-of-domain bilingual | corpus | </term> and , in addition , the <term> language | #3101 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. | |
| lr,32-1-N03-2006,ak | model </term> of an <term> in-domain monolingual | corpus | </term> . We conducted experiments with an | #3114 In order to boost the translation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus. | |
| lr,18-3-N03-2006,ak | of using an <term> out-of-domain bilingual | corpus | </term> and the possibility of using the <term> | #3144 The two evaluation measures of the BLEU score and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model. | |
| lr,10-5-N03-2025,ak | Markov Model </term> is trained on a <term> | corpus | </term> automatically tagged by the first | #3369 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner. | |
| lr,19-2-N03-4010,ak | candidates </term> from the given <term> text | corpus | </term> . The operation of the <term> system | #3683 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus. | |
| other,15-1-P03-1009,ak | classes </term> from undisambiguated <term> | corpus | data </term> . We describe a new approach | #3900 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguatedcorpus data. | |
| lr,15-5-P03-1031,ak | information </term> obtained from <term> dialogue | corpora | </term> . Unlike conventional methods that | #4234 This paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora. | |
| lr,17-2-P03-1050,ak | a <term> small ( 10K sentences ) parallel | corpus | </term> as its sole <term> training resources | #4471 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. | |
| lr,6-2-P03-1051,ak | by a <term> small manually segmented Arabic | corpus | </term> and uses it to bootstrap an <term> | #4650 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. | |
| lr,27-2-P03-1051,ak | </term> from a <term> large unsegmented Arabic | corpus | </term> . The <term> algorithm </term> uses a | #4670 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. | |
| lr,8-4-P03-1051,ak | estimated from a <term> small manually segmented | corpus | </term> of about 110,000 <term> words </term> | #4702 The language model is initially estimated from a small manually segmented corpus of about 110,000 words. | |
| lr,18-5-P03-1051,ak | from a <term> 155 million word unsegmented | corpus | </term> , and re-estimate the <term> model | #4730 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. | |
| lr,34-5-P03-1051,ak | <term> vocabulary </term> and <term> training | corpus | </term> . The resulting <term> Arabic word | #4743 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. | |
| lr,15-6-P03-1051,ak | exact match accuracy </term> on a <term> test | corpus | </term> containing 28,449 <term> word tokens | #4761 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. | |
| lr,24-7-P03-1051,ak | can create a <term> small manually segmented | corpus | </term> of the <term> language </term> of interest | #4794 We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. | |
| lr,15-2-P03-1058,ak | </term> from <term> English-Chinese parallel | corpora | </term> , which are then used for disambiguating | #4840 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. |
