| lr,2-3-I05-4010,bq | in detail . The resultant <term> bilingual | corpus | </term> , 10.4 M <term> English words </term> | #8255 The resultant bilingual corpus, 10.4M English words and 18.3M Chinese characters, is an authoritative and comprehensive text collection covering the specific and special domain of HK laws. |
| lr,6-3-P06-1052,bq | </term> . We evaluate the algorithm on a <term> | corpus | </term> , and show that it reduces the degree | #11183 We evaluate the algorithm on acorpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime. |
| lr,3-3-P05-1034,bq | component </term> . We align a <term> parallel | corpus | </term> , project the <term> source dependency | #9248 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. |
| lr,13-1-N03-2006,bq | </term> based on a small-sized <term> bilingual | corpus | </term> , we use an out-of-domain <term> bilingual | #3093 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,7-2-P05-2016,bq | required is a <term> sentence-aligned parallel | corpus | </term> . All other <term> resources </term> | #9803 The only bilingual resource required is a sentence-aligned parallel corpus. |
| lr,29-2-C88-2130,bq | </term> derived through analysis of our <term> | corpus | </term> . <term> Chart parsing </term> is <term> | #15495 The model is embodied in a program, APT, that can reproduce segments of actual tape-recorded descriptions, using organizational and discourse strategies derived through analysis of ourcorpus. |
| lr,23-2-C04-1116,bq | each author 's text as a coherent <term> | corpus | </term> . Our approach is based on the idea | #6137 This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherentcorpus. |
| lr,50-3-C04-1147,bq | phrases </term> at any distance in the <term> | corpus | </term> . The framework is flexible , allowing | #6400 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in thecorpus. |
| lr,19-2-N03-4010,bq | candidates </term> from the given <term> text | corpus | </term> . The operation of the <term> system | #3682 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus. |
| lr,30-2-C04-1192,bq | for the <term> languages </term> in the <term> | corpus | </term> . The <term> wordnets </term> are aligned | #6480 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 thecorpus. |
| lr-prod,26-4-H90-1060,bq | </term> from the <term> DARPA Resource Management | corpus | </term> . This <term> performance </term> is | #17099 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. |
| lr,15-2-C90-3063,bq | co-occurrence patterns </term> in a large <term> | corpus | </term> . To a large extent , these <term> | #16631 This paper presents an automatic scheme for collecting statistics on co-occurrence patterns in a largecorpus. |
| lr-prod,15-3-H94-1014,bq | word </term><term> Wall Street Journal text | corpus | </term> . Using the <term> BU recognition system | #21261 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus. |
| lr,19-5-J05-4003,bq | starting with a very small <term> parallel | corpus | </term> ( 100,000 <term> words </term> ) and | #9088 We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. |
| lr,12-4-C92-1055,bq | possible variations between the <term> training | corpus | </term> and the real tasks are also taken | #17893 To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members. |
| lr,7-2-P03-1051,bq | by a small <term> manually segmented Arabic | corpus | </term> and uses it to bootstrap an <term> | #4648 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,22-2-P03-1050,bq | a small ( 10K sentences ) <term> parallel | corpus | </term> as its sole <term> training resources | #4469 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,10-5-N03-2025,bq | Markov Model </term> is trained on a <term> | corpus | </term> automatically tagged by the first | #3368 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner. |
| other,15-1-P03-1009,bq | classes </term> from undisambiguated <term> | corpus | data </term> . We describe a new approach | #3899 Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguatedcorpus data. |
| tech,4-1-N06-4001,bq | strategies . We introduce a new <term> interactive | corpus | exploration tool </term> called <term> InfoMagnets | #10870 We introduce a new interactive corpus exploration tool called InfoMagnets. |
