Name
Abstract | ||
|---|---|---|
This paper presents the novel task of best topic word selection, that is the selection of the topic word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres. |
Year | Venue | Keywords |
|---|---|---|
2010 | COLING (Posters) | topic labelling,best label,best topic word selection,topic word,labelled topic,best topic word,topic model,reranking model,highest-ranked topic word,different domain,independent topic model |
Field | DocType | Volume |
Information retrieval,Visualization,Computer science,Natural language processing,Artificial intelligence,Topic model | Conference | C10-2 |
Citations | PageRank | References |
11 | 0.75 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jey Han Lau | 1 | 660 | 36.88 |
| David Newman | 2 | 1319 | 73.72 |
| Sarvnaz Karimi | 3 | 380 | 33.01 |
| Timothy Baldwin | 4 | 426 | 20.64 |