A Rule-based Question Answering System for Reading Comprehension Tests
By Ellen Riloff, Michael Thelen. 2000
184 citations recorded by the source corpus.
We have developed a rule-based system, Quarc, that can read a short story and find the sentence in the story that best answers a given question. Quarc uses heuristic rules that look for lexical and semantic clues in the question and the story. We have tested Quarc on reading comprehension tests typically given to children in grades 3--6. Overall, Quarc found the correct sentence 40% of the time, which is encouraging given the simplicity of its rules.
Subfields: Artificial Intelligence, Natural Language Processing
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In Artificial Intelligence
- Mohit Bansal
- Percy Liang
- Jianfeng Gao
- Luke S. Zettlemoyer
- Caiming Xiong
- Richard Socher
- Furu Wei
- Christopher D. Manning
In Natural Language Processing
- Mohit Bansal
- Percy Liang
- Luke S. Zettlemoyer
- Jianfeng Gao
- Jonathan Berant
- Christopher D. Manning
- Richard Socher
- Furu Wei
Citations in the corpus
- SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016, methodology): “Their pattern matching baseline was subsequently improved by a rule-based system (Riloff and Thelen, 2000) and a logistic regression model (Ng et al., 2000).”
- Reading comprehension tests for computer-based understanding evaluation (2005, unknown): “2000; Riloff 2000; Wang 2002).”
- Looking Under the Hood: Tools for Diagnosing Your Question Answering Engine (2001, background): “Riloff and Thelen, 2000; Wang et al., 2000).”
- Computational Approaches to Sentence Completion (2012, background): “, 1999) initiated a long line of research into reading comprehension based on test prep material (Charniak et al., 2000; Riloff and Thelen, 2000; Wang et al., 2000; Ng et al., 2000).”
- A Machine Learning Approach to Answering Questions for Reading Comprehension Tests (2000, methodology): “The words used in (Riloff and Thelen, 2000) are "happen", "take place" "this", "story".”
- Learning strategies for story comprehension: a reinforcement learning approach (2005, background): “Quarc [Riloff and Thelen, 2000] utilizes manually generated rules that select a sentence deemed to contain the answer based on a combination of syntactic similarity and semantic correspondence (i.”