| P10-1140 | relatively coarse segments the text | tiling algorithm | provides sufficient to identify |
| P10-1140 | domain heuris - tics . The text | tiling algorithm | divides the text into coherent |
| C96-2129 | error is the main obstacle to | tile algorithm | 's recall . A typical manifestation |
| C90-2053 | them into one process . However , | tile algorithm | needs a synchronization that |
| P10-1140 | divided into segments using the text | tiling algorithm | . Let -LSB- segs , seg2 , ... |
| C00-2135 | to 100 and 2 respectively . As | tile algorithm | proceeds , f , u ~ is adjusted |
| W02-1033 | from the inability of our n-gram | tiling algorithm | to build up the full string necessary |
| W02-1033 | Because of the effectiveness of our | tiling algorithm | over large amounts of data , |
| W02-1033 | Finally , we applied an answer | tiling algorithm | , which both merges similar answers |
| C92-4185 | each path equiv - alence . Thus , | tile algorithm | is always expensive for path |
| C00-1049 | a tightly integrated manner . | Tile algorithm | falls ill to the following broad |
| C96-1005 | the coverage and precision of | tile algorithm | at the file level might be even |
| C92-2103 | creation of the final structure | tile algorithm | works bottonl-Ill ) , 2 Motivation |
| C00-1026 | supplemented from the Chilin . 3.2 | Tile algorithm | for morphological analysis The |
| C94-1071 | appears when one looks at how | tile algorithm | ApplyTransdueer performs . In |
| C92-2066 | is randomly assigned and then | tile algorithm | will re-estimate tbese probabilities |
| C94-1091 | 1,1 this section , wc describe | tile algorithm | used for extraction of Noun Classifier |
| P98-2174 | the possible translations . The | tiling algorithm | selects the best of these translations |
| C94-2150 | it . cart be seen l \ -LSB- rom | tile algorithm | that the unrestricted grammar |
| C94-2116 | expression , " take place " . Tim way | tile algorithm | learns is tmiquc . The key to |
