| identified using a <term> phrase </term> in another | language | as a pivot . We define a <term> paraphrase | #9711 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot. | |
| lr,6-1-H92-1003,bq | describes a recently collected <term> spoken | language | corpus </term> for the <term> ATIS ( Air Travel | #18531 This paper describes a recently collected spoken language corpus for the ATIS (Air Travel Information System) domain. |
| model,1-4-P03-1051,bq | for a given <term> input </term> . The <term> | language | model </term> is initially estimated from | #4690 Thelanguage model is initially estimated from a small manually segmented corpus of about 110,000 words. |
| model,10-2-H92-1016,bq | modelling </term> , the use of a <term> bigram | language | model </term> in conjunction with a <term> | #18720 These include context-dependent phonetic modelling, the use of a bigram language model in conjunction with a probabilistic LR parser, and refinements made to the lexicon. |
| model,11-1-H01-1058,bq | address the problem of combining several <term> | language | models ( LMs ) </term> . We find that simple | #1038 In this paper, we address the problem of combining severallanguage models (LMs). |
| model,11-3-N03-2036,bq | model </term> and a <term> word-based trigram | language | model </term> . During <term> training </term> | #3441 During decoding, we use a block unigram model and a word-based trigram language model. |
| model,14-2-C92-1055,bq | approximation error </term> introduced by the <term> | language | model </term> , traditional <term> statistical | #17835 Owing to the problem of insufficient training data and approximation error introduced by thelanguage model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. |
| model,16-3-P06-4011,bq | the <term> Web </term> and building a <term> | language | model </term> of <term> abstract moves </term> | #11753 The method involves automatically gathering a large number of abstracts from the Web and building alanguage model of abstract moves. |
| model,27-3-N03-2006,bq | </term> and the possibility of using the <term> | language | model </term> . We describe a simple <term> | #3150 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 thelanguage model. |
| model,28-1-N03-2006,bq | corpus </term> and , in addition , the <term> | language | model </term> of an in-domain <term> monolingual | #3107 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, thelanguage model of an in-domain monolingual corpus. |
| model,3-1-H92-1026,bq | generative probabilistic model of natural | language | </term> , which we call <term> HBG </term> , | #18901 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. |
| model,4-3-P03-1051,bq | <term> algorithm </term> uses a <term> trigram | language | model </term> to determine the most probable | #4675 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
| model,6-1-H94-1014,bq | paper introduces a simple mixture <term> | language | model </term> that attempts to capture <term> | #21217 This paper introduces a simple mixturelanguage model that attempts to capture long distance constraints in a sentence or paragraph. |
| other,0-2-A94-1017,bq | language translation </term> . <term> Spoken | language | translation </term> requires ( 1 ) an accurate | #20211 Spoken language translation requires (1) an accurate translation and (2) a real-time response. |
| other,1-4-H01-1042,bq | </term> of <term> MT output </term> . A <term> | language | learning experiment </term> showed that <term> | #629 Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. |
| other,10-1-C94-1030,bq | speech recognition </term> of a <term> natural | language | </term> , it has been difficult to detect | #20624 In optical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. |
| other,10-2-I05-2014,bq | scarcely used for the assessment of <term> | language | pairs </term> like <term> English-Chinese </term> | #7710 Yet, they are scarcely used for the assessment oflanguage pairs like English-Chinese or English-Japanese, because of the word segmentation problem. |
| other,10-3-I05-2048,bq | <term> translation systems </term> for new <term> | language | pairs </term> or new <term> domains </term> . | #8051 This is particularly important when building translation systems for newlanguage pairs or new domains. |
| other,10-5-P01-1007,bq | of the <term> main parser </term> for a <term> | language | L </term> are directed by a <term> guide </term> | #1710 The non-deterministic parsing choices of the main parser for alanguage L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L. |
| other,11-1-A92-1027,bq | structure parsing </term> of <term> natural | language | </term> that is tailored to the problem of | #17555 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task. |
