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Unsupervised Context-Driven Question Answering Based on Link Grammar

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Artificial General Intelligence (AGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13154))

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Abstract

While general conversational intelligence (GCI) can be considered one of the core aspects of artificial general intelligence (AGI), there currently exists minimal overlap between the disciplines of AGI and natural language processing (NLP). Only a few AGI architectures can comprehend and generate natural language, and most NLP systems rely either on hardcoded, specialized rules and frameworks that cannot generalize to the various complex domains of human language or on heavily trained deep neural network models that cannot be interpreted, controlled, or made sense of. In this paper, we propose an interpretable “Contextual Generator” architecture for question answering (QA), built as an extension of the recently published “Generator” algorithm for sentence generation, that produces grammatically valid answers to queries structured as lists of seed words. We demonstrate the potential for this architecture to perform automated, closed-domain QA by detailing results on queries from SingularityNET’s “small world” POC-English corpus and from the Stanford Question Answering Dataset. Overall, our work may bring a greater degree of GCI to proto-AGI NLP pipelines. The proposed QA architecture is open-source and can be found on GitHub under the MIT License at https://github.com/aigents/aigents-java-nlp.

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Notes

  1. 1.

    https://spacy.io.

  2. 2.

    https://universaldependencies.org.

  3. 3.

    http://langlearn.singularitynet.io/data/poc-english/poc_english.txt.

  4. 4.

    http://langlearn.singularitynet.io/test/nlp/poc-english_5C_2018-06-06_0004.4.0.dict.txt.

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Author information

Authors and Affiliations

  1. Saratoga High School, Saratoga, USA

    Vignav Ramesh

  2. SingularityNET Foundation, Amsterdam, The Netherlands

    Vignav Ramesh & Anton Kolonin

  3. Aigents, Novosibirsk, Russian Federation

    Anton Kolonin

  4. Novosibirsk State University, Novosibirsk, Russian Federation

    Anton Kolonin

Authors
  1. Vignav Ramesh
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  2. Anton Kolonin
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Editor information

Editors and Affiliations

  1. SingularityNET, Amsterdam, The Netherlands

    Ben Goertzel

  2. SingularityNET, Amsterdam, The Netherlands

    Matthew Iklé

  3. SingularityNET, Amsterdam, The Netherlands

    Alexey Potapov

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Ramesh, V., Kolonin, A. (2022). Unsupervised Context-Driven Question Answering Based on Link Grammar. In: Goertzel, B., Iklé, M., Potapov, A. (eds) Artificial General Intelligence. AGI 2021. Lecture Notes in Computer Science(), vol 13154. Springer, Cham. https://doi.org/10.1007/978-3-030-93758-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-93758-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93757-7

  • Online ISBN: 978-3-030-93758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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