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Lemmatization

Lemmatization is a process of determining a base or dictionary form (lemma) for a given surface form. Especially for languages with rich morphology it is important to be able to normalize words into their base forms to better support for example search engines and linguistic studies. Main difficulties in Lemmatization arise from encountering previously unseen words during inference time as well as disambiguating ambiguous surface forms which can be inflected variants of several different base forms depending on the context.

Source: Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks

Papers

Showing 7180 of 351 papers

TitleStatusHype
Évaluation de méthodes et d’outils pour la lemmatisation automatique du français médiéval (Evaluation of methods and tools for automatic lemmatization in Old French)0
How low is too low? A monolingual take on lemmatisation in Indian languages0
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the EndangeredCode1
Learning to Lemmatize in the Word Representation Space0
Named Entity Recognition and Linking Augmented with Large-Scale Structured Data0
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions0
Enhancing Sequence-to-Sequence Neural Lemmatization with External ResourcesCode0
DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural NetworksCode0
Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task0
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language ProcessingCode1
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