2019/02/03 by Jenna Kanerva, Kanerva, Jenna, Filip Ginter +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1902.00972
openalex publication_date 2019/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we present a novel lemmatization method based on a\nsequence-to-sequence neural network architecture and morphosyntactic context\nrepresentation. In the proposed method, our context-sensitive lemmatizer\ngenerates the lemma one character at a time based on the surface form\ncharacters and its morphosyntactic features obtained from a morphological\ntagger. We argue that a sliding window context representation suffers from\nsparseness, while in majority of cases the morphosyntactic features of a word\nbring enough information to resolve lemma ambiguities while keeping the context\nrepresentation dense and more practical for machine learning systems.\nAdditionally, we study two different data augmentation methods utilizing\nautoencoder training and morphological transducers especially beneficial for\nlow resource languages. We evaluate our lemmatizer on 52 different languages\nand 76 different treebanks, showing that our system outperforms all latest\nbaseline systems. Compared to the best overall baseline, UDPipe Future, our\nsystem outperforms it on 62 out of 76 treebanks reducing errors on average by\n19% relative. The lemmatizer together with all trained models is made available\nas a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.\n