Parsing as Deduction Revisited: Using an Automatic Theorem Prover to Solve an SMT Model of a Minimalist Parser
Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
We introduce a constraint-based parser for Minimalist Grammars (MG), implemented as a working computer program, that falls within the long established “Parsing as Deduction” framework. The parser takes as input an MG lexicon and a (partially specified) pairing of sound with meaning - i.e. a word sequence paired with a semantic representation - and, using an axiomatized logic, declaratively deduces syntactic derivations (i.e. parse trees) that comport with the specified interface conditions. The parser is built on the first axiomatization of MGs to use Satisfiability Modulo Theories (SMT), encoding in a constraint-based way the principles of minimalist syntax. The parser operates via a novel solution method: it assembles an SMT model of an MG derivation, translates the inputs into SMT formulae that constrain the model, and then solves the model using the Z3 SMT-solver, a high-performance automatic theorem prover; as the SMT-model has finite size (being bounded by the inputs), it is decidable and thus solvable in finite time. The output derivation is then recovered from the model solution. To demonstrate this, we run the parser on several representative inputs and examine how the output derivations differ when the inputs are partially vs. fully specified. We conclude by discussing the parser’s extensibility and how a linguist can use it to automatically identify: (i) dependencies between input interface conditions and principles of syntax, and (ii) contradictions or redundancies between the model axioms encoding principles of syntax.
Incremental Acquisition of a Minimalist Grammar using an SMT-Solver
Proceedings of the Society for Computation in Linguistics 2022
Modeling the Ordering of English Adjectives using Collaborative Filtering
Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022)
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis
Robert C. Berwick
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
Accurate recovery of predicate-argument structure from a Universal Dependency (UD) parse is central to downstream tasks such as extraction of semantic roles or event representations. This study introduces compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. Accuracy of compchain classification serves as a proxy for measuring accurate recovery of predicate-argument structure from sentences with embedding. We analyzed the distribution of compchains in three UD English treebanks, EWT, GUM and LinES, revealing that these treebanks are sparse with respect to sentences with predicate-argument structure that includes predicate-argument embedding. We evaluated the CoNLL 2018 Shared Task UDPipe (v1.2) baseline (dependency parsing) models as compchain classifiers for the EWT, GUMS and LinES UD treebanks. Our results indicate that these three baseline models exhibit poorer performance on sentences with predicate-argument structure with more than one level of embedding; we used compchains to characterize the errors made by these parsers and present examples of erroneous parses produced by the parser that were identified using compchains. We also analyzed the distribution of compchains in 58 non-English UD treebanks and then used compchains to evaluate the CoNLL’18 Shared Task baseline model for each of these treebanks. Our analysis shows that performance with respect to compchain classification is only weakly correlated with the official evaluation metrics (LAS, MLAS and BLEX). We identify gaps in the distribution of compchains in several of the UD treebanks, thus providing a roadmap for how these treebanks may be supplemented. We conclude by discussing how compchains provide a new perspective on the sparsity of training data for UD parsers, as well as the accuracy of the resulting UD parses.
Using Collaborative Filtering to Model Argument Selection
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
This study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a predicate can take as its complement. We find that, when trained on verb-noun co-occurrence data drawn from the Corpus of Contemporary American-English (COCA), two popular model-based CF algorithms, Singular Value Decomposition and Non-negative Matrix Factorization, perform well on this task, each achieving an AUROC of at least 0.89 and surpassing several different baselines. We then show that the embedding-vectors for verbs and nouns learned by the two CF models can be quantized (via application of k-means clustering) with minimal loss of performance on the prediction task while only using a small number of verb and noun clusters (relative to the number of distinct verbs and nouns). Finally we evaluate the alignment between the quantized embedding vectors for verbs and the Levin verb classes, finding that the alignment surpassed several randomized baselines. We conclude by discussing how model-based CF algorithms might be applied to learning restrictions on constituent selection between various lexical categories and how these (learned) models could then be used to augment a (rule-based) constituency grammar.
Inferring Minimalist Grammars with an SMT-Solver
Proceedings of the Society for Computation in Linguistics 2020