Abstract
We propose a methodology to populate the verb type hierarchy of a deep computational grammar in the HPSG formalism using syntactic and morphological information from Universal Dependencies (UD) treebanks. It is exemplified by means of the UD Bosque corpus and PorGram, a computational grammar for Portuguese constructed in the LinGO Grammar Matrix framework, but it can be applied to analogous grammars of other languages using other UD treebanks. The main component of the methodology is a Python module that extracts from the annotated sentences the core arguments and other features that are relevant to determine verb valence. This module enables the creation of a Python dictionary that maps valence frames to verb objects. This dictionary facilitates not only determining which frames occur with which verbs, but also detecting annotation errors. The potential of the module for rapid expansion of the lexical coverage of PorGram and corpus annotation error detection is illustrated with concrete examples.
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We consider this to be fair use, due to the small amount of knowledge from these sources that was incorporated into PorGram.
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One of the reviewers suggested the newly released BDCamões Treebank, whose dependency graphs are also available in UD format, but with a different tag set [17]. Despite its potential advantages, it mostly a historical corpus, which makes it not immediately useful in our context, since our main concern with PorGram is modern Portuguese. Therefore, we postpone using BDCamões Treebank data for future work.
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de Alencar, L.F., Coutinho, L.R., da Silva, W.J.L., Nunes, A.L., Rademaker, A. (2022). Extracting Valences from a Dependency Treebank for Populating the Verb Lexicon of a Portuguese HPSG Grammar. In: Pinheiro, V., et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_34
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