| W01-1008 | , ( Stein et al. , 2000 ) use | topic clustering | for extrinsic evaluation . Although |
| D09-1017 | apply LM ( language model ) based | topic clustering | to group the extracted phrases |
| S14-2093 | experimental features from the commercial | topic clustering | software are useful in some cases |
| P14-1033 | sensitive to outliers . In our | topic clustering | , each data point is a topic |
| P14-1119 | strengthens the hypothesis of using | topic clustering | for keyphrase extraction . However |
| D10-1033 | recognition , machine trans - lation , | topic clustering | , and information retrieval . |
| W12-2602 | adjacency pairs ( Shriberg et | topic clustering | . In Sections 3-4 , we describe |
| D09-1017 | modified words . 4.2 LM-based | Topic Clustering | To categorize the extracted phrases |
| P14-1033 | discover knowledge in two stages : | topic clustering | and frequent pattern mining ( |
| P14-1119 | CommunityCluster , a variant of the | topic clustering | approach to keyphrase extraction |
| W12-0604 | their methods to two prob - lems , | topic clustering | of web search results and disease |
| P14-1033 | top k terms per topic used in | topic clustering | ( Section 4.1 ) . The number |
| P14-1033 | the other two sub-steps . 4.1 | Topic Clustering | After running LDA ( or AKL ) |
| D14-1214 | the early steps . For example , | topic clustering | in Section 3 not only offers |
| H94-1014 | including descriptions of automatic | topic clustering | and ro - bust.estimation techniques |
| D09-1017 | that from the NB baseline . With | topic clustering | , the COMB approach also gets |
| W02-0702 | history is calculated as : 3.2 | Topics clustering | method This method clusters topics |
| P14-5017 | techniques are adopted , e.g. | topic clustering | , heuristics algorithms , etc. |
| P09-1121 | ( Chang et al. , 2009 ) , and | topic clustering | ( Wu and Oard , 2008 ) . In this |
| H94-1014 | consider other metrics for automatic | topic clustering | , such as a word count weighted |
