Modeling Biological Processes for Reading Comprehension

  title={Modeling Biological Processes for Reading Comprehension},
  author={Jonathan Berant and V. Srikumar and P. Chen and A. V. Linden and Brittany Harding and Brad Huang and Peter Clark and Christopher D. Manning},
  • Jonathan Berant, V. Srikumar, +5 authors Christopher D. Manning
  • Published in EMNLP 2014
  • Computer Science
  • Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. [...] Key Method To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.Expand Abstract
    135 Citations

    Figures, Tables, and Topics from this paper

    Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
    • 45
    • PDF
    Question Answering as Global Reasoning Over Semantic Abstractions
    • 49
    • Highly Influenced
    • PDF
    SQuAD Reading Comprehension
    • PDF
    Machine Comprehension with Discourse Relations
    • 48
    • PDF
    Reading Comprehension with Graph-based Temporal-Casual Reasoning
    • 9
    SQuAD: 100, 000+ Questions for Machine Comprehension of Text
    • 2,495
    • PDF
    Learning Knowledge Graphs for Question Answering through Conversational Dialog
    • 89
    • PDF
    Recent Trends in Natural Language Understanding for Procedural Knowledge
    • Dena F. Mujtaba, N. Mahapatra
    • Computer Science
    • 2019 International Conference on Computational Science and Computational Intelligence (CSCI)
    • 2019
    Semantic Parsing to Probabilistic Programs for Situated Question Answering
    • 16
    • PDF
    Cross Sentence Inference for Process Knowledge
    • 5
    • PDF


    Deep Read: A Reading Comprehension System
    • 213
    • PDF
    Semantic Parsing on Freebase from Question-Answer Pairs
    • 1,016
    • PDF
    COGEX: A Logic Prover for Question Answering
    • 197
    • PDF
    Driving Semantic Parsing from the World's Response
    • 229
    • PDF
    Paraphrase-Driven Learning for Open Question Answering
    • 281
    • PDF
    Learning for Semantic Parsing with Statistical Machine Translation
    • 271
    • PDF
    Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
    • 770
    • PDF
    Learning Biological Processes with Global Constraints
    • 17
    • PDF
    MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
    • 479
    • PDF
    Learning to Automatically Solve Algebra Word Problems
    • 188
    • PDF