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Table 8. Average precision of phrase-level translation for short query Short English

in A New Hybrid Approach for Chinese-English Query Translation
by Guo-wei Bian, Hsin-hsi Chen 1998
"... In PAGE 9: ... The performance of word-level translation for short version of CACM queries is listed in Table 7. Table8 shows the performance of phrase-level translation. The 11- point average precision of the monolingual short English queries is 29.... ..."
Cited by 1

Table 5: Results of learning at phrase-levels only (i.e., without residual predictions).

in Playing Mozart by Analogy: Learning Multi-Level Timing and Dynamics Strategies
by Gerhard Widmer, Asmir Tobudic 2003
Cited by 8

Table 4. Word-level and phrase-level stress in Tiberian Hebrew

in The phonology of pitch accents in Chickasaw *
by Matthew Gordon

Table 2.1: Parsing phrase levels represented in sentential form. action

in Probabilistic Language Modeling for Generalized LR Parsing
by Virach Sornlertlamvanich 1998
Cited by 4

Table 8.5: Sentimentalism full stemming - micro analysis

in S150]] In an effort to gradually establish a small boat fleet in Adak, subsection (b) of section 803 provides that during the years 2004 through 2008, up to 25 percent of the Aleutian allocation may be harvested by vessels 60 feet or less in length overal
by Bei Yu
Cited by 1

Table 1 Range of Responses to Two Questions from the Index of Consumer Sentiment

in unknown title
by unknown authors
"... In PAGE 6: ... Higher Volatility in National Business Questions In a pattern that recurred throughout our analysis of qualitative expectations, we found much greater month-to-month volatility in responses to the macroeconomic expectations question concerning national business conditions (BUS12) than to the personal expectations question concerning family finances (PEXP). In Table1 , we show the range of frequencies (as a percentage of the sample) giving favorable or unfavorable responses, and the difference in these percentages plus 100 (that is, the Index of Consumer Sentiment relative score) during the 12 months from June 2002 to May 2003. The relative score for the national business conditions question BUS12 rose from a 12-month minimum of 65.... ..."

Table 7. Average precision of word-level translation for short query Short English

in A New Hybrid Approach for Chinese-English Query Translation
by Guo-wei Bian, Hsin-hsi Chen 1998
"... In PAGE 9: ... The equivalents of query term are selected by four different selection strategies. The performance of word-level translation for short version of CACM queries is listed in Table7 . Table 8 shows the performance of phrase-level translation.... ..."
Cited by 1

Table 2: Movie review sentiment analysis mean-absolute-error for each author. Dataset l/u/test SVR SSL Improvement

in Semisupervised regression with order preferences
by Xiaojin Zhu, Andrew B. Goldberg 2006
Cited by 1

Table 5 shows some of the extracted pairs. To evaluate the method in detail, we randomly annotated 400 sentences that contain both subjective words of GI-terms and the film terms to analysis5. In our evaluation, if the subjective stance of the extracted sentiment expressions is the same as the subjective stance of the sentence to the same target, we say the sentiment expression and the target are coherent and the sentence is mined correctly. Therefore, the definitions of precision and recall are as following. A= number of sentences that mined correctly; B= number of sentences which sentiment expression and target pairs were extracted from; C= the number of sentences that contain coherent sentiment expression and target pairs. A

in Mining the Relation between Sentiment Expression and Target Using Dependency of Words
by Zhongchao Fei, Xuanjing Huang, Lide Wu
"... In PAGE 7: ... Table 4. Statistics of extracted sentiment expression pairs(GI-Terms) Method Count Baseline1 6701 Baseline2 12545 DR-4 19556 DR-All 21257 Table5 . Samples of extracted pairs Target Sentiment Expression music cool movie no originality film avoid dialogue, editing shoddy Basline1: (RB)+JJ+(NN)+Target,((RB)+JJ)+NN+ Target.... ..."

Table 3. Translation quality between literality scores Method (A) Won (B) Won Same Quality (B) Gain Rate

in E.: Bilingual corpus cleaning focusing on translation literality
by Kenji Imamura, Eiichiro Sumita 2002
"... In PAGE 3: ....2. Results of experiments Table 2 shows the numbers of sentences contained in the cleaned corpora and acquired transfer rules. Table3 com- pares the quality of translations among the three cleaning methods. First, focusing on the sentence number after cleaning, corpus sizes were reduced to about 81% in the case of word- level cleaning and to about 87% in the case of phrase-level cleaning.... ..."
Cited by 1
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