Learning of Structurally Unambiguous Probabilistic Grammars

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5 Scopus citations
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The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar’s topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for structurally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using co-linear multiplicity tree automata (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a strucutrally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. We demonstrate the usefulness of our algorithm in learning PCFGs over genomic data.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages9
ISBN (Electronic)9781713835974
StatePublished - 18 May 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021


Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online


  • Active Learning
  • Learning Theory
  • Bioinformatics
  • Interpretaility & Analysis of NLP Models

ASJC Scopus subject areas

  • Artificial Intelligence


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