| failed , and it involves the two stages | of | 1 ) parsing a set of <term> phrases </term> | #19404 Robust parsing is applied only after a full analysis has failed, and it involves the two stages of 1) parsing a set of phrases and clauses, and 2) gluing them together to obtain a single semantic frame encoding the full meaning of the sentence. | |
| trained with a little <term> corpus </term> | of | 100,000 <term> words </term> , the system guesses | #11237 After several experiments, and trained with a little corpusof 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%. | |
| the <term> collection </term> and distribution | of | 12,000 <term> utterances </term> of <term> spontaneous | #18593 We summarize the motivation for this effort, the goals, the implementation of a multi-site data collection paradigm, and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech, natural language and spoken language | |
| construct a <term> corpus </term> consisting | of | 126,610 <term> sentences </term> . This paper | #11466 In our experiment, the method could construct a corpus consisting of 126,610 sentences. | |
| retrieval </term> indicates an improvement | of | 22-38 % in <term> average precision </term> | #4578 Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above. | |
| classifies 73.2 % in a <term> German corpus </term> | of | 2.284 <term> SRHs </term> as either coherent | #2522 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpusof 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%). | |
| and sentence error rates </term> by a factor | of | 2.5 and 1.6 , respectively , on the <term> | #18763 Together with the use of a larger training set, these modifications combined to reduce the speech recognition word and sentence error rates by a factor of 2.5 and 1.6, respectively, on the October '91 test set. | |
| tech,13-3-C92-4199,bq | word formation </term> , <term> identification | of | 2-character and 3-character Chinese names | #18329 The proposed mechanism includes title-driven name recognition, adaptive dynamic word formation, identification of 2-character and 3-character Chinese names without title. |
| questions </term> . We found a potential increase | of | 35 % in <term> MRR </term> with respect to | #10787 We found a potential increase of 35% in MRR with respect to the original question. | |
| WSJ </term> , an <term> error reduction </term> | of | 4.4 % on the best previous single automatically | #3001 Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reductionof 4.4% on the best previous single automatically learned tagging result. | |
| precision </term> of 70 % and a <term> recall </term> | of | 49 % in the task of placing <term> commas | #11272 It also gets a precision of 70% and a recallof 49% in the task of placing commas. | |
| i860 chip </term> , which provides a factor | of | 5 speed-up over a <term> SUN 4 </term> for <term> | #16936 The speech-search algorithm is implemented on a board with a single Intel i860 chip, which provides a factor of 5 speed-up over a SUN 4 for straight C code. | |
| incoherent ( given a <term> baseline </term> | of | 54.55 % ) . We propose a new <term> phrase-based | #2534 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 baselineof 54.55%). | |
| % . It also gets a <term> precision </term> | of | 70 % and a <term> recall </term> of 49 % in | #11266 It also gets a precisionof 70% and a recall of 49% in the task of placing commas. | |
| the <term> baseline model ’s score </term> | of | 88.2 % . The article also introduces a | #8857 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s scoreof 88.2%. | |
| commas </term> with a <term> precision </term> | of | 96 % and a <term> recall </term> of 98 % . | #11251 After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precisionof 96% and a recall of 98%. | |
| precision </term> of 96 % and a <term> recall </term> | of | 98 % . It also gets a <term> precision </term> | #11257 After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recallof 98%. | |
| obtaining an <term> average precision </term> | of | 98 % for retrieving correct <term> fields | #6879 We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precisionof 98% for retrieving correct fields and 87% for multi-field records. | |
| Translations </term> are produced by means | of | a <term> beam-search decoder </term> . Experimental | #7409 Translations are produced by means of a beam-search decoder. | |
| context-dependent phonetic modelling </term> , the use | of | a <term> bigram language model </term> in conjunction | #18717 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. |
