Joint Lemmatization and Morphological Tagging with LEMMING

Fuente: arXiv
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Hauptverfasser: Muller, Thomas, Cotterell, Ryan, Fraser, Alexander, Schütze, Hinrich
Format: Preprint
Veröffentlicht: 2024
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author Muller, Thomas
Cotterell, Ryan
Fraser, Alexander
Schütze, Hinrich
author_facet Muller, Thomas
Cotterell, Ryan
Fraser, Alexander
Schütze, Hinrich
contents We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages; e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial.
format Preprint
id arxiv_https___arxiv_org_abs_2405_18308
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Joint Lemmatization and Morphological Tagging with LEMMING
Muller, Thomas
Cotterell, Ryan
Fraser, Alexander
Schütze, Hinrich
Computation and Language
We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages; e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial.
title Joint Lemmatization and Morphological Tagging with LEMMING
topic Computation and Language
url https://arxiv.org/abs/2405.18308