Core techniques of question answering systems over knowledge bases: a survey

By Dennis Diefenbach, Vanessa López, Kamal Deep Singh, Pierre Maret. Knowledge and Information Systems, 2017

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f3fe27b0e10b2498c4efe6bd64e1614aee5cc251

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. Xipeng Qiu
2. Xuanjing Huang
3. Hang Yan
4. Xiaonan Li
5. Ruotian Ma
6. Gerhard Weikum
7. Abdalghani Abujabal
8. Jannik Strotgen

Citations in the corpus

• FLAT: Chinese NER Using Flat-Lattice Transformer (2020, background): “Named entity recognition (NER) plays an indispensable role in many downstream natural language processing (NLP) tasks (Chen et al., 2015; Diefenbach et al., 2018).”
• Simplify the Usage of Lexicon in Chinese NER (2019, background): “NER plays an important role in many downstream tasks, including knowledge base construction (Riedel et al., 2013), information retrieval (Chen et al., 2015), and question answering (Diefenbach et al., 2018).”
• TempQuestions: A Benchmark for Temporal Question Answering (2018, methodology): “We refer the reader to Diefenbach et al. [12] for further details.”