Rank learning for factoid question answering with linguistic and semantic constraints

By Matthew W. Bilotti, Jonathan L. Elsas, Jaime G. Carbonell, Eric Nyberg. 2010

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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. Jordan L. Boyd-Graber
2. Mohit Iyyer
3. Hal Daumé Iii
4. Mohit Bansal
5. Yoav Goldberg
6. Zhou Zhao
7. Richard Socher
8. Noah A. Smith

In Natural Language Processing

1. Mohit Iyyer
2. Jordan L. Boyd-Graber
3. Hal Daumé Iii
4. Yoav Goldberg
5. Richard Socher
6. Mohit Bansal
7. Zhou Zhao
8. Samuel R. Bowman

Citations in the corpus

• A Neural Network for Factoid Question Answering over Paragraphs (2014, methodology): “Many approaches to this problem rely on hand-crafted pattern matching and answer-type classification to narrow down the search space (Shen, 2007; Bilotti et al., 2010; Wang, 2006).”
• Community-Based Question Answering via Heterogeneous Social Network Learning (2016, background): “The benefits of cQA have been proven in (Jurczyk and Agichtein 2007; Li, Lyu, and King 2012) and cQA has attracted lots of attention in the fields of information retrieval and natural language processing research (Bilotti et al. 2010).”