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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 311320 of 351 papers

TitleStatusHype
DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural NetworksCode0
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical ResourcesCode0
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text SummarizationCode0
Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised MethodsCode0
Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw TextCode0
Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddingsCode0
Training Data Augmentation for Context-Sensitive Neural Lemmatizer Using Inflection Tables and Raw TextCode0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Stylistic Fingerprints, POS-tags and Inflected Languages: A Case Study in PolishCode0
Heidelberg-Boston @ SIGTYP 2024 Shared Task: Enhancing Low-Resource Language Analysis With Character-Aware Hierarchical TransformersCode0
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