| D13-1145 | Sporleder , 2009 ) . Most previous | idiom detection | systems have focused on specific |
| D13-1145 | These gains also translate to | idiom detection | in sen - tences , by simply using |
| P10-1116 | . Model III is applied to the | idiom detection | task since the paraphrases from |
| D13-1145 | These gains also translate to | idiom detection | in sentences , by simply using |
| D14-1216 | Previous Work Previous approaches to | idiom detection | can be classified into two groups |
| D13-1145 | idiomatic and the first to reduce | idiom detection | to identification via a dictionary |
| W13-1502 | document . hand written rules for | idiom detection | . Moreover , it is also envisaged |
| P10-1116 | framework on the related task of | idiom detection | , which involves distinguishing |
| E06-1043 | notion of syntactic fixedness for | idiom detection | , though specific to a highly |
| W13-1502 | the concepts in the text , an | idiom detection | component and a topic model compo |
| W13-1502 | presented in Figure 2 . 4.2 The | Idiom Detection | Web Service In the actual linguistic |
| J99-4005 | e.g. , lexicon construction , | idiom detection | , psychological norms , and cross-language |
| P10-1116 | Disambiguation and Token-based | Idiom Detection | </title> Linlin Li Benjamin Roth |
| D13-1145 | identification via a dictionary . Previous | idiom detection | systems fall in one of two paradigms |
