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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

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Showing 126–150 of 351 papers

TitleStatusHype
Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.—0
Harmonizing Different Lemmatization Strategies for Building a Knowledge Base of Linguistic Resources for Latin—0
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian—0
Nefnir: A high accuracy lemmatizer for Icelandic—0
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in MorphologyCode0
Development of email classifier in Brazilian Portuguese using feature selection for automatic response—0
Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages—0
Training Data Augmentation for Context-Sensitive Neural Lemmatizer Using Inflection Tables and Raw TextCode0
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings—0
Revisiting NMT for Normalization of Early English LettersCode0
Morphological parsing of low‑resource languagesCode0
Producing Corpora of Medieval and Premodern Occitan—0
A Simple Joint Model for Improved Contextual Neural Lemmatization—0
Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw TextCode0
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning—0
Improving Lemmatization of Non-Standard Languages with Joint LearningCode0
Few-Shot and Zero-Shot Learning for Historical Text Normalization—0
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks—0
Data-Driven Morphological Analysis for Uralic Languages—0
UZH@SMM4H: System Descriptions—0
A Morphological Analyzer for Shipibo-Konibo—0
Joint Learning of POS and Dependencies for Multilingual Universal Dependency ParsingCode0
Attention-free encoder decoder for morphological processing—0
NLP-Cube: End-to-End Raw Text Processing With Neural NetworksCode0
Tree-Stack LSTM in Transition Based Dependency ParsingCode0
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