Open-domain textual question answering techniques

By Sanda M. Harabagiu, Steven J. Maiorano, Marius Pasca. 2003

93 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Question Answering & Reading Comprehension

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. Tat-seng Chua
2. Soujanya Poria
3. Percy Liang
4. C. Burges
5. Wenqiang Lei
6. Mohamed Yahya
7. Matthew Richardson
8. Gerhard Weikum

In Natural Language Processing

1. Matthew Richardson
2. Tat-seng Chua
3. Mohamed Yahya
4. Soujanya Poria
5. Gerhard Weikum
6. Jianming Zheng
7. Erin Renshaw
8. Abdalghani Abujabal

In Question Answering & Reading Comprehension

1. Tat-seng Chua
2. Matthew Richardson
3. Soujanya Poria
4. Jianming Zheng
5. Erin Renshaw
6. Chao Wang
7. C. Burges
8. Fengbin Zhu

Citations in the corpus

• MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, methodology): “This dataset spurred several research efforts, for example using reinforcement learning (Grois and Wilkins, 2005), named entity resolution (Harabagiu et al., 2003) and mapping questions and answers to logical form (Wellner et al., 2006).”
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, methodology): “Question Analysis, Document Retrieval and Answer Extraction [6], [9], [11].”
• ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (2018, background): “Traditionally, the problem of QA has been explored over large textual corpora (Cui et al., 2005; Dietz and Gamari, 2017; Ferrucci, 2012; Harabagiu et al., 2001, 2003; Ravichandran and Hovy, 2002; Saquete et al., 2009; Voorhees and Tice, 2000) with answers being textual phrases.”