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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 101–150 of 351 papers

TitleStatusHype
Better Together: Modern Methods Plus Traditional Thinking in NP Alignment—0
BabyFST - Towards a Finite-State Based Computational Model of Ancient Babylonian—0
Material Philology Meets Digital Onomastic Lexicography: The NordiCon Database of Medieval Nordic Personal Names in Continental Sources—0
Overview of the EvaLatin 2020 Evaluation Campaign—0
Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac—0
Voting for POS tagging of Latin texts: Using the flair of FLAIR to better Ensemble Classifiers by Example of Latin—0
Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian—0
A Resource for Studying Chatino Verbal Morphology—0
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural ModelsCode0
\'UFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared TaskCode0
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection—0
ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared TaskCode0
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging—0
Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER—0
To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation—0
Corpora and Processing Tools for Non-standard Contemporary and Diachronic Balkan Slavic—0
Unsupervised Lemmatization as Embeddings-Based Word ClusteringCode0
Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing—0
UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging—0
CBNU System for SIGMORPHON 2019 Shared Task 2: a Pipeline Model—0
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian—0
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few—0
Morpheus: A Neural Network for Jointly Learning Contextual Lemmatization and Morphological TaggingCode0
Neural Lemmatization of Multiword Expressions—0
CUNI--Malta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation—0
Cross-Lingual Lemmatization and Morphology Tagging with Two-Stage Multilingual BERT Fine-TuningCode0
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
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
Revisiting NMT for Normalization of Early English LettersCode0
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings—0
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
Joint Learning of POS and Dependencies for Multilingual Universal Dependency ParsingCode0
NLP-Cube: End-to-End Raw Text Processing With Neural NetworksCode0
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task—0
UZH@SMM4H: System Descriptions—0
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNsCode0
Attention-free encoder decoder for morphological processing—0
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