Work

Joint Lemmatization and Morphological Tagging with Lemming

Year: 2015

Type: conference-paper

Abstract: We present LEMMING, a modular loglinear 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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d15-1272

Authors: Thomas Müller, Ryan Cotterell, Alexander Fraser, Hinrich Schütze

Institutions: Ludwig-Maximilians-Universität München, Johns Hopkins University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, US-UK Fulbright Commission, European Commission, Deutsche Forschungsgemeinschaft

Awards: G434337789, RI: Small: CompCog: Modeling Latent Discrete Knowledge Across Utterances, Health in my Language