Random-Walk Term Weighting for Improved Text Classification
By Samer Hassan, Rada Mihalcea, Carmen Banea. 2007
21 citations recorded by the source corpus.
Quite recently, a few hundreds of semantic concepts are detected automatically with varied performance and subsequently, a new video retrieval paradigm of query-by-concept emerges. In this paper, we consider the problem of exploiting the potential of learned semantics concepts, together with the combination of traditional methods, for automatic and interactive retrieval. We argue that it is important, in both automatic and interactive retrieval scenarios, to find a few relevant concepts to search with, given a multimedia query. For automatic retrieval, we show that both text and image inputs are useful for solving this query-concept-mapping (QUCOM) problem. For interactive retrieval, searching with relevant concepts plus conventional feedback methods is quite effective and is robust to initial search results. Experimental evidence on the search task of TRECVID 2006 shows that by solving QUCOM with a large lexicon of 311 semantic concept detectors, the automatic retrieval performance increases 20% and the interactive retrieval performance has the potential to outperform the state-of-the-art systems.
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- François Rousseau
- Michalis Vazirgiannis
- Emmanouil Kiagias
- Zhuyun Dai
- Roi Blanco
- Christina Lioma
- Jamie Callan
- Fragkiskos D. Malliaros
Citations in the corpus
- Text Categorization as a Graph Classification Problem (2015, methodology): “Similarly to (Hassan et al., 2007) that used graph-of-words to propose alternative weights for the n-gram features, we can capitalize on main core retention to still extract binary n-gram features for classification but considering only the terms belonging to the main core of each document.”
- Graph-based term weighting for information retrieval (2012, methodology): “…(Erkan and Radev 2004), variants and extensions of which have been applied to keyword detection, word sense disambiguation, text classification (Hassan and Banea 2006), summarisation (by extraction or query-biased Esuli and Sebastiani 2007, or ontology-based Plaza et al. 2008), novelty…”