| W13-1606 | traditionally solved by means of | supervised text classification | techniques ( Ott et al. , 2011 |
| D10-1028 | 2007 ) . Here we use them for | supervised text classification | . Specifically , we use adaptor |
| P14-5011 | extraction and machine learning for | supervised text classification | . Like DKPro TC , it can be used |
| W05-0601 | contained in the WN hierarchy in a | supervised text classification | task has been pro- posed . Intuitively |
| P14-2010 | sutanuccse iitm ac in Abstract | Supervised text classification | algorithms require a large number |
| E12-3002 | data in the target language . | Supervised text classification | requires a large amount of labeled |
| P11-1019 | treated automated assessment as a | supervised text classification | task , where training texts are |
| W13-1715 | NLI2013 shared task is framed as a | supervised text classification | problem where the set of native |
| P09-3011 | Firstly , in the application of | supervised text classification | , features can be selected by |
| P09-3011 | classifi - cation . By comparing the | supervised text classification | and unsupervised text clustering |
| P14-2010 | world datasets . 1 Introduction In | supervised text classification | learning algo - rithms , the |
| N09-1054 | made , results from classical , | supervised text classification | experiments are mixed ( Mullen |
| W13-1902 | Language Processing ( NLP ) and | supervised text classification | methods to identify patients |
| P14-2010 | <title> Sprinkling Topics for Weakly | Supervised Text Classification | </title> swapnil hingmiretcs |
| P15-2096 | age-appropriatness ratings is to treat it as a | supervised text classification | task : first , a corpus of song |
| N09-1054 | analytical approaches in NLP such as | supervised text classification | ( Mullen and Malouf , 2006 ) |
| W09-1123 | model , or hypothesis , or rule . | Supervised text classification | is a machine learning technique |
