Keyword extraction from a single document using word co-occurrence statistical information
By Yutaka Matsuo, Mitsuru Ishizuka. Int. J. Artif. Intell. Tools, 2004
888 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/df64be1e1c486038380199d124fc2b15536adfcc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vincent Ng
- Kazi Saidul Hasan
- Timothy Baldwin
- Su Nam Kim
- Olena Medelyan
- Jaime G. Carbonell
- Nick Cramer
- Yingyu Pan
In Molecular Biology
- Vincent Ng
- Kazi Saidul Hasan
- Jaime G. Carbonell
- Timothy Baldwin
- Nick Cramer
- Yingyu Pan
- Su Nam Kim
- Wendy Cowley
Citations in the corpus
- Automatic Keyword Extraction from Individual Documents (2010, methodology): “Mihalcea and Tarau (2004) report that TextRank achieves its best performance when only nouns and adjectives are selected as potential keywords.”
- Anomaly Based Web Phishing Page Detection (2006, methodology): “[7, 9, 12, 14]) exploit document information, including the hierarchical structure of documents, the frequency of terms, the cooccurrences of terms etc.”
- Automatic Keyphrase Extraction: A Survey of the State of the Art (2014, methodology): “Researchers have computed relatedness between candidates using co-occurrence counts (Mihalcea and Tarau, 2004; Matsuo and Ishizuka, 2004) and semantic relatedness (Grineva et al., 2009), and represented the relatedness information collected from a document as a graph (Mihalcea and Tarau, 2004; Wan…”
- SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles (2010, methodology): “…(Frank et al., 1999; Witten et al., 1999; Turney, 1999; Hulth, 2003; Turney, 2003; Park et al., 2004; Barker and Corrnacchia, 2000; Hulth, 2004; Matsuo and Ishizuka, 2004; Mihalcea and Tarau, 2004; Medelyan and Witten, 2006; Nguyen and Kan, 2007; Wan and Xiao, 2008; Liu et al., 2009; Medelyan,…”
- Deep Keyphrase Generation (2017, neutral): “…importance to the document, either through supervised or unsupervised machine learning meth-ods with a set of manually-defined features (Frank et al., 1999; Liu et al., 2009, 2010; Kelleher and Luz, 2005; Matsuo and Ishizuka, 2004; Mihalcea and Tarau, 2004; Song et al., 2003; Witten et al., 1999).”
- Survey on supervised machine learning techniques for automatic text classification (2019, background): “J48, an improved different of C4.5 can be resulted by Matsuo and Ishizuka (2004).”
- Self reinforcement for important passage retrieval (2013, methodology): “Several unsupervised key phrase methods have been proposed, such as language modeling, graph-based ranking and clustering [16].”