Richard Mörbitz


2021

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Supertagging-based Parsing with Linear Context-free Rewriting Systems
Thomas Ruprecht | Richard Mörbitz
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constituents establish a new state of the art. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. We describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure that transforms lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is evaluated on the English Discontinuous Penn Treebank and the German treebanks Negra and Tiger.

2020

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Lexicalization of Probabilistic Linear Context-free Rewriting Systems
Richard Mörbitz | Thomas Ruprecht
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

In the field of constituent parsing, probabilistic grammar formalisms have been studied to model the syntactic structure of natural language. More recently, approaches utilizing neural models gained lots of traction in this field, as they achieved accurate results at high speed. We aim for a symbiosis between probabilistic linear context-free rewriting systems (PLCFRS) as a probabilistic grammar formalism and neural models to get the best of both worlds: the interpretability of grammars, and the speed and accuracy of neural models. To combine these two, we consider the approach of supertagging that requires lexicalized grammar formalisms. Here, we present a procedure which turns any PLCFRS G into an equivalent lexicalized PLCFRS G’. The derivation trees in G’ are then mapped to equivalent derivations in G. Our construction for G’ preserves the probability assignment and does not increase parsing complexity compared to G.

2019

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Weighted parsing for grammar-based language models
Richard Mörbitz | Heiko Vogler
Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing

We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs flexible weight algebras. It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. We show an algorithm which terminates and is correct for a large class of weighted grammar-based language models.