Mining knowledge from text using information extraction

By Raymond J. Mooney, Razvan C. Bunescu. SKDD, 2005

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/41a441ce0081c8a829a982dc19c60131bfbde046

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

1. B. Liu
2. Nitin Jindal
3. Bing Liu
4. Marius Pasca
5. Benjamin Van Durme
6. Yejin Choi
7. Geli Fei
8. Leman Akoglu

In Molecular Biology

1. B. Liu
2. Marius Pasca
3. Nitin Jindal
4. Benjamin Van Durme

Citations in the corpus

• Mining Comparative Sentences and Relations (2006, positive): “Our results show that LSRs outperform Conditional Random Fields (CRF) (Lafferty, McCallum & Pereira 2001), which is perhaps the most effective extraction method so far (Mooney & Bunescu 2005).”
• Weakly-Supervised Acquisition of Open-Domain Classes and Class Attributes from Web Documents and Query Logs (2008, result): “In contrast, the textual data sources used in previous studies in large-scale information extraction are either Web documents (Mooney and Bunescu, 2005; Banko et al., 2007) or, recently, query logs (Paşca, 2007), but not both.”
• Collective knowledge systems: Where the Social Web meets the Semantic Web (2008, neutral): “Let us consider in more detail these two ways that Semantic Web techniques can create value for collective knowledge systems.”